| 1. |
- Klionsky, Daniel J., et al.
(författare)
-
Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
-
Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
-
Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
|
|
| 2. |
- Van de Casteele, M., et al.
(författare)
-
Neurogenin 3(+) cells contribute to beta-cell neogenesis and proliferation in injured adult mouse pancreas
- 2013
-
Ingår i: Cell Death and Disease. - London : Nature Publishing Group. - 2041-4889. ; 4, s. e523-
-
Tidskriftsartikel (refereegranskat)abstract
- We previously showed that injury by partial duct ligation (PDL) in adult mouse pancreas activates Neurogenin 3 (Ngn3)(+) progenitor cells that can differentiate to beta cells ex vivo. Here we evaluate the role of Ngn3(+) cells in beta cell expansion in situ. PDL not only induced doubling of the beta cell volume but also increased the total number of islets. beta cells proliferated without extended delay (the so-called 'refractory' period), their proliferation potential was highest in small islets, and 86% of the beta cell expansion was attributable to proliferation of pre-existing beta cells. At sufficiently high Ngn3 expression level, upto 14% of all beta cells and 40% of small islet beta cells derived from non-beta cells. Moreover, beta cell proliferation was blunted by a selective ablation of Ngn3(+) cells but not by conditional knockout of Ngn3 in pre-existing beta cells supporting a key role for Ngn3(+) insulin(-) cells in beta cell proliferation and expansion. We conclude that Ngn3(+) cell-dependent proliferation of pre-existing and newly-formed beta cells as well as reprogramming of non-beta cells contribute to in vivo beta cell expansion in the injured pancreas of adult mice.
|
|
| 3. |
- van Ommen, Ruud, et al.
(författare)
-
Time-series analysis of pressure fluctuations in gas-solid fluidized beds - A review
- 2011
-
Ingår i: International Journal of Multiphase Flow. - : Elsevier BV. - 0301-9322. ; 37:5, s. 403-428
-
Tidskriftsartikel (refereegranskat)abstract
- This work reviews methods for time-series analysis for characterization of the dynamics of gas-solid fluidized beds from in-bed pressure measurements for different fluidization regimes. The paper covers analysis in time domain, frequency domain, and in state space. It is a follow-up and an update of a similar review paper written a decade ago. We use the same pressure time-series as used by Johnsson et al. (2000). The paper updates the previous review and includes additional methods for time-series analysis, which have been proposed to investigate dynamics of gas-solid fluidized beds. Results and underlying assumptions of the methods are discussed. Analysis in the time domain is often the simplest approach. The standard deviation of pressure fluctuations is widely used to identify regimes in fluidized beds, but its disadvantage is that it is an indirect measure of the dynamics of the flow. The so-called average cycle time provides information about the relevant time scales of the system, making it an easy-to-calculate alternative to frequency analysis. Autoregressive methods can be used to show an analogy between a fluidized bed and a single or a set of simple mechanical systems acting in parallel. The most common frequency domain method is the power spectrum. We show that - as an alternative to the often used non-parametric methods to estimate the power spectrum - parametric methods can be useful. To capture transient effects on a longer time scale (>1 s), either the transient power spectral density or wavelet analysis can be applied. For the state space analysis, the information given by the Kolmogorov entropy is equivalent to that of the average frequency, obtained in the frequency domain. However, an advantage of certain state space methods, such as attractor comparison, is that they are more sensitive to small changes than frequency domain methods; this feature can be used for, e.g., on-line monitoring. In general, we conclude that, over the past decade, progress has been made in understanding fluidized-bed dynamics by extracting the relevant information from pressure fluctuation data, but the picture is still incomplete.
|
|
