Automation in high‐content flow cytometry screening

Naumann, Ulrike; Wand, M. P.

doi:10.1002/cyto.a.20754

Cited by 27 publications

(25 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After applying identical rectangular laser beam side-and forward-scatter gates to compensate for fragmented and dead cells and a boundary filter to remove oversaturated data, we performed a sine hyperbolic function transformation for the ␣-SMA immunoreactivity/ Alexa 488.A channel of the data. An unsupervised model-based clustering approach described by Naumann and coworkers [curvHDR, (54,55)] was then employed to group the peripheral lung fibroblasts based on similarity in the ␣-SMA immunoreactivity levels. This method classes cell characteristics by first identifying data group boundaries that have statistically significant high negative curvatures and then refining the region limits by examining local density functions.…”

Section: Methodsmentioning

confidence: 99%

Soluble guanylate cyclase modulates alveolarization in the newborn lung

Bachiller

Cornog²,

Kato

et al. 2013

American Journal of Physiology-Lung Cellular and Molecular Phys

View full text Add to dashboard Cite

Nitric oxide (NO) regulates lung development through incompletely understood mechanisms. NO controls pulmonary vascular smooth muscle cell (SMC) differentiation largely through stimulating soluble guanylate cyclase (sGC) to produce cGMP and increase cGMP-mediated signaling. To examine the role of sGC in regulating pulmonary development, we tested whether decreased sGC activity reduces alveolarization in the normal and injured newborn lung. For these studies, mouse pups with gene-targeted sGC-α1 subunit truncation were used because we determined that they have decreased pulmonary sGC enzyme activity. sGC-α1 knockout (KO) mouse pups were observed to have decreased numbers of small airway structures and lung volume compared with wild-type (WT) mice although lung septation and body weights were not different. However, following mild lung injury caused by breathing 70% O2, the sGC-α1 KO mouse pups had pronounced inhibition of alveolarization, as evidenced by an increase in airway mean linear intercept, reduction in terminal airway units, and decrease in lung septation and alveolar openings, as well as reduced somatic growth. Because cGMP regulates SMC phenotype, we also tested whether decreased sGC activity reduces lung myofibroblast differentiation. Cellular markers revealed that vascular SMC differentiation decreased, whereas myofibroblast activation increased in the hyperoxic sGC-α1 KO pup lung. These results indicate that lung development, particularly during hyperoxic injury, is impaired in mouse pups with diminished sGC activity. These studies support the investigation of sGC-targeting agents as therapies directed at improving development in the newborn lung exposed to injury.

show abstract

Section: Methodsmentioning

confidence: 99%

Soluble guanylate cyclase modulates alveolarization in the newborn lung

Bachiller

Cornog²,

Kato

et al. 2013

American Journal of Physiology-Lung Cellular and Molecular Phys

View full text Add to dashboard Cite

show abstract

“…Using the r = 0 normal scale rule, as illustrated in Table 1, could lead to insufficient smoothing for large sample sizes. In more comprehensive analyses of these data, described in Naumann and Wand (2009), more sophisticated filters for identifying cellular signatures are employed. The normal scale rule for second derivative estimation plays an important role in the initial phases of these filters, identifying candidate modal regions of possible interest.…”

Section: Application: High-throughput Flow Cytometrymentioning

confidence: 99%

Asymptotics for general multivariate kernel density derivative estimators

Chacón¹,

Duong²,

Wand³

2011

STAT Sinica

Self Cite

117

103

View full text Add to dashboard Cite

We investigate general kernel density derivative estimators, that is, kernel estimators of multivariate density derivative functions using general (or unconstrained) bandwidth matrix selectors. These density derivative estimators have been relatively less well researched than their density estimator analogues. A major obstacle for progress has been the intractability of the matrix analysis when treating higher order multivariate derivatives. With an alternative vectorization of these higher order derivatives, these mathematical intractabilities are surmounted in an elegant and unified framework. The finite sample and asymptotic analysis of squared errors for density estimators are generalized to density derivative estimators. Moreover, we are able to exhibit a closed form expression for a normal scale bandwidth matrix for density derivative estimators. These normal scale bandwidths are employed in a numerical study to demonstrate the gain in performance of unconstrained selectors over their constrained counterparts.

show abstract

“…1), as is usually the case in aquatic samples (Bouvier et al 2007). Although other automated techniques are unbiased for population shape, they are not able to detect overlapping groups (Naumann & Wand 2009, Naumann et al 2010, Sugár & Sealfon 2010, Ge & Sealfon 2012. Finally, other methods cannot be applied to large datasets due to computational efficiency (Zare et al 2010).…”

Section: Discussionmentioning

confidence: 99%

Automated clustering of heterotrophic bacterioplankton in flow cytometry data

García¹,

López‐Urrutia²,

Morán³

2014

Aquat. Microb. Ecol.

View full text Add to dashboard Cite

Flow cytometry has become a standard method to analyze bacterioplankton. Analysis of samples by flow cytometry is automatic, but it is followed by manual classification of the bacterioplankton groups in flow cytometry standard (FCS) files. This classification is a time consuming and subjective task performed by manually drawing the limits of the groups present in cytograms, a process referred to as gating. The automation of flow cytometry data processing based on pattern recognition techniques could provide an efficient tool to overcome some of these disadvantages. Here, we propose the use of model-based clustering techniques for the automatic detection of low (LNA) and high (HNA) nucleic acid bacterioplankton groups in FCS files. To validate our method, we compared the automatic classification with a flow cytometry database from a 9 yr time series collected in the central Cantabrian Sea that had been manually analyzed. The correlation between automatic and manual gating methods was > 0.9 for cell counts and 0.7 to 0.95 for side scatter values, a proxy of cell size. In addition, no significant differences were found in the mean annual cycle of LNA and HNA cell abundance depicted by both methods. We also quantified the subjectivity of manual gating. The coefficient of variation for heterotrophic bacteria counts obtained by different analysts was around 10 to 20%. Our results suggest that the combination of flow cytometry and automatic gating provides a valuable tool to analyze microbial communities objectively and accurately, allowing us to safely compare bacterioplankton samples from different environments.

show abstract

Automation in high‐content flow cytometry screening

Cited by 27 publications

References 13 publications

Soluble guanylate cyclase modulates alveolarization in the newborn lung

Soluble guanylate cyclase modulates alveolarization in the newborn lung

Asymptotics for general multivariate kernel density derivative estimators

Automated clustering of heterotrophic bacterioplankton in flow cytometry data

Contact Info

Product

Resources

About