High-speed automatic characterization of rare events in flow cytometric data

Qi, Yuan; Fang, Youhan; Sinclair, David R.; Guo, Shangqin; Alberich-Jordà, Meritxell; Lü, Jun; Tenen, Daniel G.; Kharas, Michael G.; Pyne, Saumyadipta

doi:10.1371/journal.pone.0228651

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Indeed, data generated are not only high dimensional (i.e., involving multiparametric panels) but also simultaneously at the single-cell level and considerably high throughput (hundreds of thousands of cells per sample). A large cytometric sample can result in inefficient coverage in the detection of a number of spurious small populations (often outliers of larger, noisy populations) (Qi et al, 2020). Moreover, tuning the parameters of the analysis could be very effective for rare populations (Baumgaertner et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Antigen‐Specific T and B Cells for Monitoring Immune Protection Against SARS‐CoV‐2

et al. 2023

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Immunological memory is the basis of protection against most pathogens. Long‐living memory T and B cells able to respond to specific stimuli, as well as persistent antibodies in plasma and in other body fluids, are crucial for determining the efficacy of vaccination and for protecting from a second infection by a previously encountered pathogen. Antigen‐specific cells are represented at a very low frequency in the blood, and indeed, they can be considered “rare events” present in the memory T‐cell pool. Therefore, such events should be analyzed with careful attention. In the last 20 years, different methods, mostly based upon flow cytometry, have been developed to identify such rare antigen‐specific cells, and the COVID‐19 pandemic has given a dramatic impetus to characterize the immune response against the virus. In this regard, we know that the identification, enumeration, and characterization of SARS‐CoV‐2‐specific T and B cells following infection and/or vaccination require i) the use of specific peptides and adequate co‐stimuli, ii) the use of appropriate inhibitors to avoid nonspecific activation, iii) the setting of appropriate timing for stimulation, and iv) the choice of adequate markers and reagents to identify antigen‐specific cells. Optimization of these procedures allows not only determination of the magnitude of SARS‐CoV‐2‐specific responses but also a comparison of the effects of different combinations of vaccines or determination of the response provided by so‐called “hybrid immunity,” resulting from a combination of natural immunity and vaccine‐generated immunity. Here, we present two methods that are largely used to monitor the response magnitude and phenotype of SARS‐CoV‐2‐specific T and B cells by polychromatic flow cytometry, along with some tips that can be useful for the quantification of these rare events. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Identification of antigen‐specific T cells Basic Protocol 2: Identification of antigen‐specific B cells

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Section: Discussionmentioning

confidence: 99%

Analysis of Antigen‐Specific T and B Cells for Monitoring Immune Protection Against SARS‐CoV‐2

et al. 2023

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“…Indeed, straightforward extensions are feasible for similar circular data such as RNFL phenotypes and other optic neuropathies as well as related eye imaging platforms, e.g., OCT-Angiography (OCTA). As we have demonstrated for other biomedical platforms 51 , 56 – 59 , the new pipeline CIFU could be enhanced incrementally with different functionalities, say, to increase computational efficiency or capture the perspective of the clinical experts. The circular curve visualization introduced in the present study may lead to a more user-friendly tool for clinical purposes as we plan to make it interactive, with advanced capabilities to jointly handle data and metadata, in our future work.…”

Section: Discussionmentioning

confidence: 99%

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Ali

Wainwright

Petersen

et al. 2021

Sci Rep

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Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40–49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.

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Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Ali

Wainwright

Petersen

et al. 2021

Preprint

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Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by the clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Optical coherence tomography (OCT) is a popular and quantitative eye imaging platform for precise and reproducible measurement of such parameters in the clinic. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and thus present challenges to clinical decision-making. To address this, large and diverse normative OCT databases with mathematically precise description of phenotypes can help with early detection and characterization of the different phenotypes that are encountered in the clinic. In this study, we generated a new large dataset of OCT generated high-resolution circular data on NRR phenotypes, along with other clinical covariates, of nearly 4,000 healthy eyes as part of a well-established clinical cohort (LVPEI-GLEAMS) of Asian Indian participants. In this study, we (1) generated high-resolution circular OCT measurements of NRR thickness in a given eye, (2) introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis that is demonstrated using the OCT dataset, and (3) addressed the disparity of representation of the Asian Indian population in normative OCT databases. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, meta-clustering to characterize the clustering output using clinical covariates, and presenting a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma.

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High-speed automatic characterization of rare events in flow cytometric data

Cited by 3 publications

References 23 publications

Analysis of Antigen‐Specific T and B Cells for Monitoring Immune Protection Against SARS‐CoV‐2

Analysis of Antigen‐Specific T and B Cells for Monitoring Immune Protection Against SARS‐CoV‐2

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

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