Dataset Information

Assessment of Protocol Impact on Subjectivity Uncertainty When Analyzing Peripheral Blood Mononuclear Cell Flow Cytometry Data Files.

ABSTRACT: Measured variability of product within Cell and Gene Therapy (CGT) manufacturing arises from numerous sources across pre-analytical to post-analytical phases of testing. Operators are a function of the manufacturing process and are an important source of variability as a result of personal differences impacted by numerous factors. This research uses measurement uncertainty in comparison to Coefficient of Variation to quantify variation of participants when they complete Flow Cytometry data analysis through a 5-step gating sequence. Two study stages captured participants applying gates using their own judgement, and then following a diagrammatical protocol, respectively. Measurement uncertainty was quantified for each participant (and analysis phase) by following Guide to the Expression of Uncertainty in Measurement protocols, combining their standard deviations in quadrature from each gating step in the respective protocols. When participants followed a diagrammatical protocol, variation between participants reduced by 57%, increasing confidence in a more uniform reported cell count percentage. Measurement uncertainty provided greater resolution to the analysis processes, identifying that most variability contributed in the Flow Cytometry gating process is from the very first gate, where isolating target cells from dead or dying cells is required. This work has demonstrated the potential for greater usage of measurement uncertainty within CGT manufacturing scenarios, due to the resolution it provides for root cause analysis and continuous improvement.

SUBMITTER: Grant R

PROVIDER: S-EPMC8103269 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Pathological processes in hematologic diseases originate at the single-cell level, often making measurements on individual cells more clinically relevant than population averages from bulk analysis. For this reason, flow cytometry has been an effective tool for single-cell analysis of properties using light scattering and fluorescence labeling. However, conventional flow cytometry cannot measure cell mechanical properties, alterations of which contribute to the pathophysiology of hematologic diseases such as sepsis, diabetic retinopathy, and sickle cell anemia. Here we present a high-throughput microfluidics-based 'biophysical' flow cytometry technique that measures single-cell transit times of blood cell populations passing through in vitro capillary networks. To demonstrate clinical relevance, we use this technique to characterize biophysical changes in two model disease states in which mechanical properties of cells are thought to lead to microvascular obstruction: (i) sepsis, a process in which inflammatory mediators in the bloodstream activate neutrophils and (ii) leukostasis, an often fatal and poorly understood complication of acute leukemia. Using patient samples, we show that cell transit time through and occlusion of microfluidic channels is increased for both disease states compared to control samples, and we find that mechanical heterogeneity of blood cell populations is a better predictor of microvascular obstruction than average properties. Inflammatory mediators involved in sepsis were observed to significantly affect the shape and magnitude of the neutrophil transit time population distribution. Altered properties of leukemia cell subpopulations, rather than of the population as a whole, were found to correlate with symptoms of leukostasis in patients-a new result that may be useful for guiding leukemia therapy. By treating cells with drugs that affect the cytoskeleton, we also demonstrate that their transit times could be significantly reduced. Biophysical flow cytometry offers a low-cost and high-throughput diagnostic and drug discovery platform for hematologic diseases that affect microcirculatory flow.

Dataset Information

Assessment of Protocol Impact on Subjectivity Uncertainty When Analyzing Peripheral Blood Mononuclear Cell Flow Cytometry Data Files.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets