Measuring Size Distribution in Highly Heterogeneous Systems with Fluorescence Correlation Spectroscopy

Sengupta, Parijat; Garai, Kanchan; Balaji, J.; Periasamy, N.; Maiti, Sudipta

doi:10.1016/s0006-3495(03)75006-1

Cited by 202 publications

(248 citation statements)

References 20 publications

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“…Due to the inherent heterogeneity of the cellular medium, it is difficult to use a model with a well‐defined number of diffusing species. To make no assumptions about this number, curves were first fitted by using a model that relies on the Maximum Entropy approach (Sengupta et al , 2003). In this case, autocorrelation curves were analysed assuming a quasicontinuous distribution of diffusing components ( D i with i >> 1).…”

Section: Methodsmentioning

confidence: 99%

Coactivators and general transcription factors have two distinct dynamic populations dependent on transcription

et al. 2017

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SAGA and ATAC are two distinct chromatin modifying co‐activator complexes with distinct enzymatic activities involved in RNA polymerase II (Pol II) transcription regulation. To investigate the mobility of co‐activator complexes and general transcription factors in live‐cell nuclei, we performed imaging experiments based on photobleaching. SAGA and ATAC, but also two general transcription factors (TFIID and TFIIB), were highly dynamic, exhibiting mainly transient associations with chromatin, contrary to Pol II, which formed more stable chromatin interactions. Fluorescence correlation spectroscopy analyses revealed that the mobile pool of the two co‐activators, as well as that of TFIID and TFIIB, can be subdivided into “fast” (free) and “slow” (chromatin‐interacting) populations. Inhibiting transcription elongation decreased H3K4 trimethylation and reduced the “slow” population of SAGA, ATAC, TFIIB and TFIID. In addition, inhibiting histone H3K4 trimethylation also reduced the “slow” populations of SAGA and ATAC. Thus, our results demonstrate that in the nuclei of live cells the equilibrium between fast and slow population of SAGA or ATAC complexes is regulated by active transcription via changes in the abundance of H3K4me3 on chromatin.

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Section: Methodsmentioning

confidence: 99%

Coactivators and general transcription factors have two distinct dynamic populations dependent on transcription

et al. 2017

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“…S5 and S6). The curves were fitted using a maximum entropy method (MEM) (25) to extract the apparent hydrodynamic radius (R H ) of soluble species released from fibrils during depolymerization (see SI Appendix for details).…”

Section: Significancementioning

confidence: 99%

pH-induced molecular shedding drives the formation of amyloid fibril-derived oligomers

Tipping

Karamanos

Jakhria

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

107

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Amyloid disorders cause debilitating illnesses through the formation of toxic protein aggregates. The mechanisms of amyloid toxicity and the nature of species responsible for mediating cellular dysfunction remain unclear. Here, using β 2 -microglobulin (β 2 m) as a model system, we show that the disruption of membranes by amyloid fibrils is caused by the molecular shedding of membrane-active oligomers in a process that is dependent on pH. Using thioflavin T (ThT) fluorescence, NMR, EM and fluorescence correlation spectroscopy (FCS), we show that fibril disassembly at pH 6.4 results in the formation of nonnative spherical oligomers that disrupt synthetic membranes. By contrast, fibril dissociation at pH 7.4 results in the formation of nontoxic, native monomers. Chemical cross-linking or interaction with hsp70 increases the kinetic stability of fibrils and decreases their capacity to cause membrane disruption and cellular dysfunction. The results demonstrate how pH can modulate the deleterious effects of preformed amyloid aggregates and suggest why endocytic trafficking through acidic compartments may be a key factor in amyloid disease.amyloid | fibrils | disassembly | oligomer | membrane disruption

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“…We first applied the MEMFCS fitting method [27,37] to autocorrelation functions at different time points throughout the experiment (Fig. 3A).…”

Section: Comparison Of Memfcs and 5gdm Fittingmentioning

confidence: 99%

Simultaneous measurement of a range of particle sizes during Aβ1–42 fibrillogenesis quantified using fluorescence correlation spectroscopy

Mittag¹,

Milani²,

Walsh

et al. 2014

Biochemical and Biophysical Research Communications

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a b s t r a c tLow molecular weight oligomers of amyloid beta (Ab) are important drivers of Alzheimer's disease. A decrease in Ab monomer levels in human cerebrospinal fluid (CSF) is observed in Alzheimers' patients and is a robust biomarker of the disease. It has been suggested that the decrease in monomer levels in CSF is due to the formation of Ab oligomers. A robust technique capable of identifying Ab oligomers in CSF is therefore desirable. We have used fluorescence correlation spectroscopy and a five Gaussian distribution model (5GDM) to monitor the aggregation of Ab 1-42 in sodium phosphate buffer and in artificial cerebrospinal fluid (ACSF). In buffer, several different sized components (monomer, oligomers, protofibrils and fibrils) can be identified simultaneously using 5GDM. In ACSF, the faster kinetics of fibrillogenesis leads to the formation of fibrils on very short timescales. This analysis method can also be used to monitor the aggregation of other proteins, nanoparticles or colloids, even in complex biological fluids.

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Measuring Size Distribution in Highly Heterogeneous Systems with Fluorescence Correlation Spectroscopy

Cited by 202 publications

References 20 publications

Coactivators and general transcription factors have two distinct dynamic populations dependent on transcription

Coactivators and general transcription factors have two distinct dynamic populations dependent on transcription

pH-induced molecular shedding drives the formation of amyloid fibril-derived oligomers

Simultaneous measurement of a range of particle sizes during Aβ1–42 fibrillogenesis quantified using fluorescence correlation spectroscopy

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