1. |
|
|
2. |
- Adler, Jeremy, et al.
(author)
-
Colocalization Analysis in Fluorescence Microscopy
- 2012
-
In: Cell Imaging Techniques. - New York : Humana Press. - 9781627030557 ; , s. 97-109
-
Book chapter (peer-reviewed)abstract
- The measurement of colocalization requires images of two fluorophores that are aligned, with no cross talk, and that the intensities remain within the response range of the microscope. Quantitation depends upon differentiating between the presence and absence of fluorescence, and measurements should be made within biologically relevant regions of interest. Co-occurrence can be measured simply by area or with the M1 and M2 coefficients, and should be compared to random distributions. Correlation analysis should use the Pearson and Spearman coefficients, which need to be measured by replicate based noise corrected correlation to eliminate errors arising from differences in image quality. Ideally, both co-occurrence and correlation should be reported.
|
|
3. |
|
|
4. |
- Adler, Jeremy, et al.
(author)
-
Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous.
- 2019
-
In: Communications biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 2
-
Journal article (peer-reviewed)abstract
- Cells are neither flat nor smooth, which has serious implications for prevailing plasma membrane models and cellular processes like cell signalling, adhesion and molecular clustering. Using probability distributions from diffusion simulations, we demonstrate that 2D and 3D Euclidean distance measurements substantially underestimate diffusion on non-flat surfaces. Intuitively, the shortest within surface distance (SWSD), the geodesic distance, should reduce this problem. The SWSD is accurate for foldable surfaces but, although it outperforms 2D and 3D Euclidean measurements, it still underestimates movement on deformed surfaces. We demonstrate that the reason behind the underestimation is that topographical features themselves can produce both super- and subdiffusion, i.e. the appearance of anomalous diffusion. Differentiating between topography-induced and genuine anomalous diffusion requires characterising the surface by simulating Brownian motion on high-resolution cell surface images and a comparison with the experimental data.
|
|
5. |
|
|
6. |
|
|
7. |
|
|
8. |
- Adler, Jeremy, et al.
(author)
-
Membrane topography and the overestimation of protein clustering in single molecule localisation microscopy - identification and correction
- 2024
-
In: Communications Biology. - : Springer Nature. - 2399-3642. ; 7:1
-
Journal article (peer-reviewed)abstract
- According to single-molecule localisation microscopy almost all plasma membrane proteins are clustered. We demonstrate that clusters can arise from variations in membrane topography where the local density of a randomly distributed membrane molecule to a degree matches the variations in the local amount of membrane. Further, we demonstrate that this false clustering can be differentiated from genuine clustering by using a membrane marker to report on local variations in the amount of membrane. In dual colour live cell single molecule localisation microscopy using the membrane probe DiI alongside either the transferrin receptor or the GPI-anchored protein CD59, we found that pair correlation analysis reported both proteins and DiI as being clustered, as did its derivative pair correlation-photoactivation localisation microscopy and nearest neighbour analyses. After converting the localisations into images and using the DiI image to factor out topography variations, no CD59 clusters were visible, suggesting that the clustering reported by the other methods is an artefact. However, the TfR clusters persisted after topography variations were factored out. We demonstrate that membrane topography variations can make membrane molecules appear clustered and present a straightforward remedy suitable as the first step in the cluster analysis pipeline. Variations in membrane topography can lead to the overestimation of protein clustering which can be avoided using a second image of a membrane marker, demonstrated with simulations and live cell SMLM.
|
|
9. |
|
|
10. |
- Adler, Jeremy, et al.
(author)
-
Noise and colocalization in fluorescence microscopy: solving a problem
- 2008
-
In: Microscopy and Analysis. ; 22:5, s. 7-10
-
Research review (other academic/artistic)abstract
- The Pearson correlation coefficient (PCC) is regularly used in colocalization measurements, but it is sensitive to image noise. Images of fluorophores are usually degraded by Poisson and background noise and we have found that, even with apparently high quality images, the measured PCC is substantially understated, to the extent that the numbers become misleading. This means that ostensibly significant differences in the PCC between two populations may just reflect differing image quality while dissimilar levels of noise may mask significant differences. A new correction, based on measurements of image quality, derived from a pair of images for each fluorophore, aligns the measured PCC with the true PCC. Our method, the Replicate Based Noise Corrected Correlation (RBNCC), generates an accurate PCC even from poor images. It is highly photon efficient and therefore well suited for use in live cell fluorescence imaging or with rapidly bleaching fluorophores.
|
|