Methodology of high‐dimensional flow cytometry in monitoring immune microenvironment of pituitary neuroendocrine tumors DOI Creative Commons
Marina Loguinova,

Valeria Mazeeva,

Daria Lisina

et al.

Cytometry Part B Clinical Cytometry, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 2025

Abstract Characterization of the tumor immune microenvironment (TIME) pituitary neuroendocrine tumors (PitNETs) is crucial for understanding behavior different types PitNETs and identification possible causes their aggressiveness, rapid growth, resistance to therapy. High‐dimensional flow cytometry (FC) a promising technology studying TIME but poses unique technical challenges, especially when applied solid tissues PitNETs, in particular. This paper evaluates potential FC analyzing by addressing methodological difficulties across all stages workflow proposing solutions. We developed protocol preparing single‐cell suspensions from PitNET FC. involved optimization enzymatic digestion comparison it with mechanical tissue dissociation assessing cell yield, viability, target antigen expression. designed four multicolor panels analyze major lymphocyte myeloid subsets including determination subpopulations T, B, NK cells activation cytotoxic potential, neutrophils, monocytes, CD68 + CD64 CD11b low macrophages M2 M1 subtypes, two suppressor ‐ PMN‐MDSC M‐MDSC. Principles panel design, spreading error, importance voltage balance proper cytometer setting are discussed. The were validated demonstrated feasibility simultaneous use on surgical comprehensive characterization. compared frequencies blood, three sequential eluates find out contamination level samples blood leukocytes. To address we propose strategy logical data gating that removes spurious signals aggregates, dead cells, subcellular debris can interfere analysis. Our results indicate despite difficulties, multiparametric effectively characterize PitNETs. enhanced infiltrate provides valuable insights into biology advances clinical diagnostics.

Language: Английский

Methodology of high‐dimensional flow cytometry in monitoring immune microenvironment of pituitary neuroendocrine tumors DOI Creative Commons
Marina Loguinova,

Valeria Mazeeva,

Daria Lisina

et al.

Cytometry Part B Clinical Cytometry, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 2025

Abstract Characterization of the tumor immune microenvironment (TIME) pituitary neuroendocrine tumors (PitNETs) is crucial for understanding behavior different types PitNETs and identification possible causes their aggressiveness, rapid growth, resistance to therapy. High‐dimensional flow cytometry (FC) a promising technology studying TIME but poses unique technical challenges, especially when applied solid tissues PitNETs, in particular. This paper evaluates potential FC analyzing by addressing methodological difficulties across all stages workflow proposing solutions. We developed protocol preparing single‐cell suspensions from PitNET FC. involved optimization enzymatic digestion comparison it with mechanical tissue dissociation assessing cell yield, viability, target antigen expression. designed four multicolor panels analyze major lymphocyte myeloid subsets including determination subpopulations T, B, NK cells activation cytotoxic potential, neutrophils, monocytes, CD68 + CD64 CD11b low macrophages M2 M1 subtypes, two suppressor ‐ PMN‐MDSC M‐MDSC. Principles panel design, spreading error, importance voltage balance proper cytometer setting are discussed. The were validated demonstrated feasibility simultaneous use on surgical comprehensive characterization. compared frequencies blood, three sequential eluates find out contamination level samples blood leukocytes. To address we propose strategy logical data gating that removes spurious signals aggregates, dead cells, subcellular debris can interfere analysis. Our results indicate despite difficulties, multiparametric effectively characterize PitNETs. enhanced infiltrate provides valuable insights into biology advances clinical diagnostics.

Language: Английский

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