Linearity and noise sources in flow cytometry

Ubezio, Paolo; Andreoni, Alessandra

doi:10.1002/cyto.990060205

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…[6] is i n agreement with the formula proposed in Ref. (17), where the different contributions to cv2 of staining and other instrumental factors are specified.…”

Section: The Mathematical Modelsupporting

confidence: 76%

Automatic analysis of flow cytometric DNA histograms from irradiated mouse male germ cells

et al. 1989

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An automatic procedure for recovering the DNA content distribution of mouse irradiated testis cells from flow cytometric histograms is presented. First, a suitable mathematical model is developed, to represent the pattern of DNA content and fluorescence distribution in the sample. Then a parameter estimation procedure, based on the maximum likelihood approach, is constructed by means of an optimization technique. This procedure has been applied to a set of DNA histograms relative to different doses of 0.4-MeV neutrons and to different time intervals after irradiation. In each case, a good agreement between the measured histograms and the corresponding fits has been obtained. The results indicate that the proposed method for the quantitative analysis of germ cell DNA histograms can be usefully applied to the study of the cytotoxic and mutagenic action of agents of toxicological interest such as ionizing radiations.

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“…[6] is i n agreement with the formula proposed in Ref. (17), where the different contributions to cv2 of staining and other instrumental factors are specified.…”

Section: The Mathematical Modelsupporting

confidence: 76%

Automatic analysis of flow cytometric DNA histograms from irradiated mouse male germ cells

et al. 1989

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“…However, since FC observation of single cells in flow involves detection of photons emitted by the fluorescent molecules on the surface of or inside the cells, the errors cannot be distributed this way. The noise variance is not identical for every measured cell, and it is not identical for high- and low-intensity signals (18,19). …”

Section: Resultsmentioning

confidence: 99%

Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices

Novo¹,

Grégori²,

Rajwa³

2013

Cytometry Pt A

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Multispectral and hyperspectral flow cytometry (FC) instruments allow measurement of fluorescence or Raman spectra from single cells in flow. As with conventional FC, spectral overlap results in the measured signal in any given detector being a mixture of signals from multiple labels present in the analyzed cells. In contrast to traditional polychromatic FC, these devices utilize a number of detectors (or channels in multispectral detector arrays) that is larger than the number of labels, and no particular detector is a priori dedicated to the measurement of any particular label. This data-acquisition modality requires a rigorous study and understanding of signal formation as well as unmixing procedures that are employed to estimate labels abundance. The simplest extension of the traditional compensation procedure to multispectral data sets is equivalent to an ordinary least-square (LS) solution for estimating abundance of labels in individual cells. This process is identical to the technique employed for unmixing spectral data in various imaging fields. The present study shows that multispectral FC data violate key assumptions of the LS process, and use of the LS method may lead to unmixing artifacts, such as population distortion (spreading) and the presence of negative values in biomarker abundances. Various alternative unmixing techniques were investigated, including relative-error minimization and variance-stabilization transformations. The most promising results were obtained by performing unmixing using Poisson regression with an identity-link function within a generalized linear model framework. This formulation accounts for the presence of Poisson noise in the model of signal formation and subsequently leads to superior unmixing results, particularly for dim fluorescent populations. The proposed Poisson unmixing technique is demonstrated using simulated 8-channel, 2-fluorochrome data and real 32-channel, 6-fluorochrome data. The quality of unmixing is assessed by computing absolute and relative errors, as well as by calculating the symmetrized Kullback–Leibler divergence between known and approximated populations. These results are applicable to any flow-based system with more detectors than labels where Poisson noise is the dominant contributor to the overall system noise and highlight the fact that explicit incorporation of appropriate noise models is the key to accurately estimating the true label abundance on the cells.

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“…flow rate) that may change the signal shape while keeping scalar properties such as the height constant. Such shortcomings have limited UQ studies to those effects due to photodetectors and total uncertainties as characterized by population histograms [24][25][26][27].…”

Section: Metrics In the Context Of Past Workmentioning

confidence: 99%

Reproducibility in Cytometry: Signals Analysis and its Connection to Uncertainty Quantification

Patrone¹,

DiSalvo²,

Kearsley³

et al. 2022

Preprint

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Signals analysis for cytometry remains a challenging task that has a significant impact on uncertainty. Conventional cytometers assume that individual measurements are well characterized by simple properties such as the signal area, width, and height. However, these approaches have difficulty distinguishing inherent biological variability from instrument artifacts and operating conditions. As a result, it is challenging to quantify uncertainty in the properties of individual cells and perform tasks such as doublet deconvolution. We address these problems via signals analysis techniques that use scale transformations to: (I) separate variation in biomarker expression from effects due to flow conditions and particle size; (II) quantify reproducibility associated with a given laser interrogation region; (III) estimate uncertainty in measurement values on a per-event basis; and (IV) extract the singlets that make up a multiplet. The key idea behind this approach is to model how variable operating conditions deform the signal shape and then use constrained optimization to "undo" these deformations for measured signals; residuals to this process characterize reproducibility. Using a recently developed microfluidic cytometer, we demonstrate that these techniques can account for instrument and measurand induced variability with a residual uncertainty of less than 2.5% in the signal shape and less than 1% in integrated area.

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Linearity and noise sources in flow cytometry

Cited by 17 publications

References 16 publications

Automatic analysis of flow cytometric DNA histograms from irradiated mouse male germ cells

Automatic analysis of flow cytometric DNA histograms from irradiated mouse male germ cells

Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices

Reproducibility in Cytometry: Signals Analysis and its Connection to Uncertainty Quantification

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