Serial flow cytometry in an inertial focusing optofluidic microchip for direct assessment of measurement variations

DiSalvo, Matthew; Patrone, Paul N.; Kearsley, Anthony J.; Cooksey, Gregory A.

doi:10.1039/d1lc01169c

Cited by 3 publications

(11 citation statements)

References 29 publications

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“…Considering, for example, the microfluidic cytometer in Ref. [11], we assume that Eq. ( 1) can be reduced to…”

Section: A Physical Effects Causing Signal Variationmentioning

confidence: 99%

“…2 See Ref. [11] for relevant details associated with the device studied in this manuscript, including in particular the focusing strategy used to ensure that particles do not cross streamlines.…”

Section: A Physical Effects Causing Signal Variationmentioning

confidence: 99%

“…For noise-less signals and a known set of transformation parameters ∆t, R, R 0 , v, and v 0 , Eq. (11) implies that…”

Section: Signal Matchingmentioning

confidence: 99%

“…II, we consider a collection of 1302 events measured in the optofluidic device described in Ref. [11]. The measurands are 15.3 µm diameter polystyrene microspheres with dispersed Dragon Green fluorophore.…”

Section: Validation With Experimental Datamentioning

confidence: 99%

“…Using a recently developed microfluidic cytometer (see Ref. [11]), we illustrate how this analysis can: (i) quantify phenomena such as noise in flow conditions and sample variability; and (ii) estimate the per-event reproducibility associated with all remaining sources of uncertainty. We also demonstrate that this analysis provides a foundation for more advanced signal processing techniques by using it to extract the individual singlets comprising a multiplet signal, i.e.…”

Section: Introductionmentioning

confidence: 99%

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

Patrone¹,

DiSalvo²,

Kearsley³

et al. 2022

Preprint

Self Cite

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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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“…Considering, for example, the microfluidic cytometer in Ref. [11], we assume that Eq. ( 1) can be reduced to…”

Section: A Physical Effects Causing Signal Variationmentioning

confidence: 99%

Section: A Physical Effects Causing Signal Variationmentioning

confidence: 99%

“…For noise-less signals and a known set of transformation parameters ∆t, R, R 0 , v, and v 0 , Eq. (11) implies that…”

Section: Signal Matchingmentioning

confidence: 99%

Section: Validation With Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Patrone¹,

DiSalvo²,

Kearsley³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Reproducibility in cytometry: Signals analysis and its connection to uncertainty quantification

Patrone,

DiSalvo,

Kearsley

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

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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Microfluidic Blood Separation: Key Technologies and Critical Figures of Merit

Torres-Castro,

Acuña-Umaña,

Lesser-Rojas

et al. 2023

Micromachines

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Blood is a complex sample comprised mostly of plasma, red blood cells (RBCs), and other cells whose concentrations correlate to physiological or pathological health conditions. There are also many blood-circulating biomarkers, such as circulating tumor cells (CTCs) and various pathogens, that can be used as measurands to diagnose certain diseases. Microfluidic devices are attractive analytical tools for separating blood components in point-of-care (POC) applications. These platforms have the potential advantage of, among other features, being compact and portable. These features can eventually be exploited in clinics and rapid tests performed in households and low-income scenarios. Microfluidic systems have the added benefit of only needing small volumes of blood drawn from patients (from nanoliters to milliliters) while integrating (within the devices) the steps required before detecting analytes. Hence, these systems will reduce the associated costs of purifying blood components of interest (e.g., specific groups of cells or blood biomarkers) for studying and quantifying collected blood fractions. The microfluidic blood separation field has grown since the 2000s, and important advances have been reported in the last few years. Nonetheless, real POC microfluidic blood separation platforms are still elusive. A widespread consensus on what key figures of merit should be reported to assess the quality and yield of these platforms has not been achieved. Knowing what parameters should be reported for microfluidic blood separations will help achieve that consensus and establish a clear road map to promote further commercialization of these devices and attain real POC applications. This review provides an overview of the separation techniques currently used to separate blood components for higher throughput separations (number of cells or particles per minute). We present a summary of the critical parameters that should be considered when designing such devices and the figures of merit that should be explicitly reported when presenting a device’s separation capabilities. Ultimately, reporting the relevant figures of merit will benefit this growing community and help pave the road toward commercialization of these microfluidic systems.

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Serial flow cytometry in an inertial focusing optofluidic microchip for direct assessment of measurement variations

Abstract: We present a microfluidic serial flow cytometer that repeats measurements on flowing objects for per-object uncertainty quantification. The automated system assessed measurement precision as a function of its flow focusing operating parameters.

Cited by 3 publications

References 29 publications

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

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

Reproducibility in cytometry: Signals analysis and its connection to uncertainty quantification

Microfluidic Blood Separation: Key Technologies and Critical Figures of Merit

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