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Amyloid fibril length distribution quantified by atomic force microscopy single-particle image analysis.

Xue WF, Homans SW, Radford SE - Protein Eng. Des. Sel. (2009)

Bottom Line: They are of key interest not only because of their association with numerous disorders, such as type II diabetes mellitus, Alzheimer's and Parkinson's diseases, but also because of their potential to become engineered high-performance nano-materials.The method described employs single-particle image analysis corrected for the length-dependent bias that is a common problem associated with surface-based imaging techniques.The results suggest that the Weibull distribution is a suitable model in describing fibril length distributions, and reveal that fibril fragmentation is an important process even under unagitated conditions.

View Article: PubMed Central - PubMed

Affiliation: Astbury Centre for Structural Molecular Biology, Institute of Molecular and Cellular Biology, University of Leeds, Leeds LS29JT, UK. w.f.xue@leeds.ac.uk

ABSTRACT
Amyloid fibrils are proteinaceous nano-scale linear aggregates. They are of key interest not only because of their association with numerous disorders, such as type II diabetes mellitus, Alzheimer's and Parkinson's diseases, but also because of their potential to become engineered high-performance nano-materials. Methods to characterise the length distribution of nano-scale linear aggregates such as amyloid fibrils are of paramount importance both in understanding the biological impact of these aggregates and in controlling their mechanical properties as potential nano-materials. Here, we present a new quantitative approach to the determination of the length distribution of amyloid fibrils using tapping-mode atomic force microscopy. The method described employs single-particle image analysis corrected for the length-dependent bias that is a common problem associated with surface-based imaging techniques. Applying this method, we provide a detailed characterisation of the length distribution of samples containing long-straight fibrils formed in vitro from beta(2)-microglobulin. The results suggest that the Weibull distribution is a suitable model in describing fibril length distributions, and reveal that fibril fragmentation is an important process even under unagitated conditions. These results demonstrate the significance of quantitative length distribution measurements in providing important new information regarding amyloid assembly.

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Processing of the fibril length data obtained from height images exemplified by samples 1, 2, 6 and 12. (A) Frequency histograms of observed, unbinned fibril length data, illustrating the probability density of the observed length distributions. (B) Frequency histograms shown together with the cumulative frequency plots of the observed fibril lengths. (C) Unit area histograms of the observed fibril lengths, obtained by normalising the frequency histograms in B by sample size and bin size, shown together with the cumulative probability plot of the observed fibril length, obtained by normalising the cumulative frequency plots in B with sample size. (D) Unit area histograms and cumulative probability plot of fibril length after bias correction for detection of fibrils of different length using the method outlined in the text. The cumulative probability of the observed lengths (the same as in C) is also shown as grey lines for comparison.
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GZP026F2: Processing of the fibril length data obtained from height images exemplified by samples 1, 2, 6 and 12. (A) Frequency histograms of observed, unbinned fibril length data, illustrating the probability density of the observed length distributions. (B) Frequency histograms shown together with the cumulative frequency plots of the observed fibril lengths. (C) Unit area histograms of the observed fibril lengths, obtained by normalising the frequency histograms in B by sample size and bin size, shown together with the cumulative probability plot of the observed fibril length, obtained by normalising the cumulative frequency plots in B with sample size. (D) Unit area histograms and cumulative probability plot of fibril length after bias correction for detection of fibrils of different length using the method outlined in the text. The cumulative probability of the observed lengths (the same as in C) is also shown as grey lines for comparison.

Mentions: TM-AFM images of long-straight β2m fibrils deposited on freshly cleaved mica surfaces were collected at a resolution of 1024 × 1024 pixels over 10 × 10 µm areas as described in the Materials and methods section. A total of 76 height images were collected from 12 different samples containing fibrils of identical morphology but of varying length. To ensure that the same amount of fibrils in terms of initial monomer concentration or weight is present in the samples, each sample was carefully prepared by applying agitation subsequent to seeded fibril growth (described in the Materials and methods section). Under the solution conditions employed, small soluble oligomers or large non-fibrillar aggregates are not observed in the fibril samples (Smith et al., 2006a; Xue et al., 2008), and virtually all (>95%) of the initial monomers are incorporated into fibrils (Smith et al., 2006a), further ensuring equal mass concentration of fibrils present in every sample. Figure 1 shows typical height image of each of the 12 samples. From these height images, the length and the height along the highest ridge of individual fibrils, unambiguously traced according to criteria described in the Materials and methods section, were measured. Depending on the length of the fibrils in each sample, 4–16 images were collected and 374–1298 fibrils were successfully traced for each sample, with 20–340 fibrils successfully traced on each image (again depending on fibril length). A total of 9298 fibrils were traced and analysed. In Fig. 2A, the measured length L of traced fibrils for samples 1, 2, 6 and 12, as examples, is plotted in unbinned frequency histograms to illustrate the connection between the raw fibril length data and the probability density of the observed length probability distribution in each case. For each sample, the measured length of fibrils, L, can be regarded as a continuous random variable independently drawn from an underlying probability distribution characterised by its cumulative distribution function, FL(l) = P(L≤l), or its probability density function, fL(l) = dFL(l)/dl, where l represents the length and P the probability. The goal of the length distribution analysis described herein is thus to find FL(l) and fL(l) of a probability distribution model that can empirically describe the probability of finding fibrils with length L in each sample analysed.


Amyloid fibril length distribution quantified by atomic force microscopy single-particle image analysis.

Xue WF, Homans SW, Radford SE - Protein Eng. Des. Sel. (2009)

Processing of the fibril length data obtained from height images exemplified by samples 1, 2, 6 and 12. (A) Frequency histograms of observed, unbinned fibril length data, illustrating the probability density of the observed length distributions. (B) Frequency histograms shown together with the cumulative frequency plots of the observed fibril lengths. (C) Unit area histograms of the observed fibril lengths, obtained by normalising the frequency histograms in B by sample size and bin size, shown together with the cumulative probability plot of the observed fibril length, obtained by normalising the cumulative frequency plots in B with sample size. (D) Unit area histograms and cumulative probability plot of fibril length after bias correction for detection of fibrils of different length using the method outlined in the text. The cumulative probability of the observed lengths (the same as in C) is also shown as grey lines for comparison.
© Copyright Policy - creative-commons
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getmorefigures.php?uid=PMC2719499&req=5

GZP026F2: Processing of the fibril length data obtained from height images exemplified by samples 1, 2, 6 and 12. (A) Frequency histograms of observed, unbinned fibril length data, illustrating the probability density of the observed length distributions. (B) Frequency histograms shown together with the cumulative frequency plots of the observed fibril lengths. (C) Unit area histograms of the observed fibril lengths, obtained by normalising the frequency histograms in B by sample size and bin size, shown together with the cumulative probability plot of the observed fibril length, obtained by normalising the cumulative frequency plots in B with sample size. (D) Unit area histograms and cumulative probability plot of fibril length after bias correction for detection of fibrils of different length using the method outlined in the text. The cumulative probability of the observed lengths (the same as in C) is also shown as grey lines for comparison.
Mentions: TM-AFM images of long-straight β2m fibrils deposited on freshly cleaved mica surfaces were collected at a resolution of 1024 × 1024 pixels over 10 × 10 µm areas as described in the Materials and methods section. A total of 76 height images were collected from 12 different samples containing fibrils of identical morphology but of varying length. To ensure that the same amount of fibrils in terms of initial monomer concentration or weight is present in the samples, each sample was carefully prepared by applying agitation subsequent to seeded fibril growth (described in the Materials and methods section). Under the solution conditions employed, small soluble oligomers or large non-fibrillar aggregates are not observed in the fibril samples (Smith et al., 2006a; Xue et al., 2008), and virtually all (>95%) of the initial monomers are incorporated into fibrils (Smith et al., 2006a), further ensuring equal mass concentration of fibrils present in every sample. Figure 1 shows typical height image of each of the 12 samples. From these height images, the length and the height along the highest ridge of individual fibrils, unambiguously traced according to criteria described in the Materials and methods section, were measured. Depending on the length of the fibrils in each sample, 4–16 images were collected and 374–1298 fibrils were successfully traced for each sample, with 20–340 fibrils successfully traced on each image (again depending on fibril length). A total of 9298 fibrils were traced and analysed. In Fig. 2A, the measured length L of traced fibrils for samples 1, 2, 6 and 12, as examples, is plotted in unbinned frequency histograms to illustrate the connection between the raw fibril length data and the probability density of the observed length probability distribution in each case. For each sample, the measured length of fibrils, L, can be regarded as a continuous random variable independently drawn from an underlying probability distribution characterised by its cumulative distribution function, FL(l) = P(L≤l), or its probability density function, fL(l) = dFL(l)/dl, where l represents the length and P the probability. The goal of the length distribution analysis described herein is thus to find FL(l) and fL(l) of a probability distribution model that can empirically describe the probability of finding fibrils with length L in each sample analysed.

Bottom Line: They are of key interest not only because of their association with numerous disorders, such as type II diabetes mellitus, Alzheimer's and Parkinson's diseases, but also because of their potential to become engineered high-performance nano-materials.The method described employs single-particle image analysis corrected for the length-dependent bias that is a common problem associated with surface-based imaging techniques.The results suggest that the Weibull distribution is a suitable model in describing fibril length distributions, and reveal that fibril fragmentation is an important process even under unagitated conditions.

View Article: PubMed Central - PubMed

Affiliation: Astbury Centre for Structural Molecular Biology, Institute of Molecular and Cellular Biology, University of Leeds, Leeds LS29JT, UK. w.f.xue@leeds.ac.uk

ABSTRACT
Amyloid fibrils are proteinaceous nano-scale linear aggregates. They are of key interest not only because of their association with numerous disorders, such as type II diabetes mellitus, Alzheimer's and Parkinson's diseases, but also because of their potential to become engineered high-performance nano-materials. Methods to characterise the length distribution of nano-scale linear aggregates such as amyloid fibrils are of paramount importance both in understanding the biological impact of these aggregates and in controlling their mechanical properties as potential nano-materials. Here, we present a new quantitative approach to the determination of the length distribution of amyloid fibrils using tapping-mode atomic force microscopy. The method described employs single-particle image analysis corrected for the length-dependent bias that is a common problem associated with surface-based imaging techniques. Applying this method, we provide a detailed characterisation of the length distribution of samples containing long-straight fibrils formed in vitro from beta(2)-microglobulin. The results suggest that the Weibull distribution is a suitable model in describing fibril length distributions, and reveal that fibril fragmentation is an important process even under unagitated conditions. These results demonstrate the significance of quantitative length distribution measurements in providing important new information regarding amyloid assembly.

Show MeSH
Related in: MedlinePlus