Quality-Controlled Sputum Analysis by Flow Cytometry

Grayson, Marcia H.; Lai, Shao-Chiang; Bederka, Lydia H.; Araújo, Pedro Henrique de; Sanchez, Jamila R.; Reveles, Xavier T.; Rebel, Vivienne I.; Rebeles, Jennifer

doi:10.3791/62785

Cited by 3 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 ). The FCM component of the test consists of two assay tubes, one labelled with blood cell markers and one with epithelial cell markers [ 25 ]. Cells in both tubes were also labeled with anti-CD45 antibodies which selectively bind leukocytes, a viability dye to eliminate dead cells, including squamous epithelial cells (SECs) [ 27 ], and TCPP to identify cancer or cancer-associated cells [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

“…Sputum was dissociated and labeled using recently published protocols [ 25 , 26 ]. Briefly, sputum samples were incubated with a mixture of 0.1% dithiothreitol and 0.5% N -acetyl- l -cysteine for 15 min at room temperature and neutralized with Hank’s Balanced Salt Solution (HBSS).…”

Section: Methodsmentioning

confidence: 99%

“…Sputum samples were acquired on a BD LSRII flow cytometer (BD Biosciences) equipped with four lasers (405 nm, 488 nm, 561 nm, and 633 nm) or on a Navios EX (Beckman Coulter Life Sciences) equipped with three lasers (405 nm, 488 nm and 638 nm). The settings used on each flow cytometer had been previously shown to generate similar sputum profiles [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

Lemieux¹,

Reveles²,

Rebeles³

et al. 2023

Respir Res

Self Cite

View full text Add to dashboard Cite

Background Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases. Methods Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer. Results Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83–0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89–0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71–0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state. Conclusion CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

Lemieux¹,

Reveles²,

Rebeles³

et al. 2023

Respir Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sputum was liquefied using pre-warmed 0.1% dithiothreitol (DTT) at a 1:4 ratio with sputum weight (w/v) and pre-warmed 0.5% N-acetyl-L-cysteine (NAC) at a ratio of 1:1 (w/v), as described previously [ 43 ]. The resulting cell suspension was filtered through 100 μm nylon cell strainers (Falcon, Corning Inc.) to eliminate larger debris while minimizing cell loss ( S1 Fig ).…”

Section: Methodsmentioning

confidence: 99%

“…Cell labeling was performed as described previously [ 43 ]. Briefly, the majority of cells were split into two tubes: one including markers to interrogate the leukocyte (CD45 + ) cell compartment and one for the epithelial (CD45 - ) cell compartment.…”

Section: Methodsmentioning

confidence: 99%

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment

Bederka¹,

Sanchez²,

Rebeles³

et al. 2022

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Low dose computed tomography (LDCT) is the standard of care for lung cancer screening in the United States (US). LDCT has a sensitivity of 93.8% but its specificity of 73.4% leads to potentially harmful follow-up procedures in patients without lung cancer. Thus, there is a need for additional assays with high accuracy that can be used as an adjunct to LDCT to diagnose lung cancer. Sputum is a biological fluid that can be obtained non-invasively and can be dissociated to release its cellular contents, providing a snapshot of the lung environment. We obtained sputum from current and former smokers with a 30+ pack-year smoking history and who were either confirmed to have lung cancer or at high risk of developing the disease. Dissociated sputum cells were counted, viability determined, and labeled with a panel of markers to separate leukocytes from non-leukocytes. After excluding debris and dead cells, including squamous epithelial cells, we identified reproducible population signatures and confirmed the samples’ lung origin. In addition to leukocyte and epithelial-specific fluorescent antibodies, we used the highly fluorescent meso-tetra(4-carboxyphenyl) porphyrin (TCPP), known to preferentially stain cancer (associated) cells. We looked for differences in cell characteristics, population size and fluorescence intensity that could be useful in distinguishing cancer samples from high-risk samples. We present our data demonstrating the feasibility of a flow cytometry platform to analyze sputum in a high-throughput and standardized matter for the diagnosis of lung cancer.

show abstract

Implementation of flow cytometry testing on rare matrix samples: Special considerations and best practices when the sample is unique or difficult to obtain

Devitt,

Kern,

Kajstura

et al. 2024

Cytometry Part B Clinical

View full text Add to dashboard Cite

The publication of Clinical and Laboratory Standards Institute's guideline H62 has provided the flow cytometry community with much‐needed guidance on development and validation of flow cytometric assays (CLSI, 2021). It has also paved the way for additional exploration of certain topics requiring additional guidance. Flow cytometric analysis of rare matrices, or unique and/or less frequently encountered specimen types, is one such topic and is the focus of this manuscript. This document is the result of a collaboration subject matter experts from a diverse range of backgrounds and seeks to provide best practice consensus guidance regarding these types of specimens. Herein, we define rare matrix samples in the setting of flow cytometric analysis, address validation implications and challenges with these samples, and describe important considerations of using these samples in both clinical and research settings.

show abstract

Quality-Controlled Sputum Analysis by Flow Cytometry

Cited by 3 publications

References 0 publications

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment

Implementation of flow cytometry testing on rare matrix samples: Special considerations and best practices when the sample is unique or difficult to obtain

Contact Info

Product

Resources

About