NetNotes
structures are included, such as nucleoli (Pearson=0.67), or even the outline of the whole nucleus (Pearson=0.82), the numbers are clearly way off. Tis can be (sort of) avoided with the threshold- based methods if one is meticulous and makes sure only the spots are thresholded and not the entire nucleus. Tricky! For what it’s worth, the numbers above were generated with this simple Im- ageJ macro that just calculates the Pearson coefficient between the first two channels in an image (within a rectangular selection of choice):
https://drive.google.com/file/d/1Ft1CS5uFElakRſt- W3gJLSrgVbf5WHhm. It is a viable high-throughput alternative to the overly complicated colocalization plugins in Fiji. Remem- ber, the numbers obtained have absolutely no meaning unless compared to appropriate controls, more controls, and still more controls! Zdenek Svindrych
zdedenn@gmail.com
Hi Zdenek, I have to voice my strong disagreement with part
of your statement “While Pearson coefficient is one of my favor- ites (insensitive to gain, offset; no need for thresholding), the dark pixels do indeed count!” For offset and gain you are correct. With- in a wide range they are unimportant. However, the assertion that there is no need for thresholding is wrong. You are correct in stat- ing that they do affect the measured correlation. 1) Consider a negatively correlated dataset, where one molecule is abundant, and the other is weak - and vice versa - on a scatterplot with a nice -45-degree line and an appropriately measured negative Pearson correlation. Now include some pixels with neither molecule. Tese form a nice cluster at the bottom leſt of the scatter plot that is disconnected from the -45-degree set of points. Te Pearson correlation now becomes much more positive with the shiſt de- pending on how many empty pixels that are included. Te num- ber of empty pixels affects the measured correlation despite there being a negative relationship wherever both molecules are found together. 2) Conceptually, pixels with neither molecule provide no indication about the relationship between them when both are present. Tis is usually what we are really interested in. Te empty pixels simply report that there are no molecules in an area, which is adequately reported by coefficients that measure co-occurrence. Area of overlap, M1 and M2, including empty pixels, increases the measured correlation except when the correlation in the popula- tion of pixels is near perfect. Ten it has no effect. 3) Try this. A simulation where two images each have blobs with random in- tensity thrown in at random positions and Pearson is repeatedly measured as the images fill with blobs. Te algorithm means that a priori we know there is no correlation between the intensities in the two images, which is exactly that we find if the Pearson Cor- relation is measured only from pixels in which both molecules are present. If empty pixels, or pixels with one of the two molecules present are included, a quite different Pearson Correlation results. 4) Or this: two molecules are uncorrelated with high intensity re- sulting in a nice cloud of points in the center of the scatterplot and the measured correlation is around zero. Add a cluster of empty pixels and the combined correlation becomes strongly positive, even though each population measured alone is around zero. 5) Te problem with empty pixels is the values for both molecules are similar even though neither is present. Worse, the calculation of the Pearson Correlation involves subtracting the mean inten- sity over the ROI from the values, so the empty pixels make a large contribution to the measured correlation. In the first two scatter- plots you showed the correlation is clearly positive, but the empty voxels increase the magnitude of the correlation. Tis is dis- cussed at length in
http://doi.org/10.1371/journal.pone.0111983. Jeremy Adler
jeremy.adler@
igp.uu.se
2022 July •
www.microscopy-today.com
I just skimmed this paper and it seems very readable for stu-
dents, etc. However, I think that a key sentence, “Voxels larger than those specified by the Nyquist criteria will under sample the image, create artifacts, and result in false colocalizations,” requires clarification. I would not say FALSE colocalizations; I would say colocalizations limited by the sampling rate. Te ques- tion is to what extent the colocalization is biologically relevant and this varies based on the questions being asked. For instance, a few weeks ago a student came to me with images of puncta in cells that were clearly colocalized. Tis was accurate for the im- ages she had that were taken with a low N.A. 20X lens. With- in the limits of the imaging modality, the molecules of interest were colocalized and this may have been a meaningful answer. It definitely was a meaningful answer at the sampling rate of the system, and it provided valuable information about localization at the organelle scale within individual cells. But it was not the answer she needed because her biological question was one that can only be answered by a higher spatial resolution. Switching to a 63X N.A. 1.4 lens clearly showed that the green and red were mostly in different structures. Maybe they were always in differ- ent structures or possibly the same structures sometimes? What’s next, FRET, SMLM? And, of course, other types of assays such as co-IPs. Michael Cammer
michael.cammer@med.nyu
You seem to have already enough material to address the
problem of using MIPs and to measure colocalization. I personally like the YouTube movie of Ben, and the suggestions to include the correction of noise and blur (deconvolution). However, these imag- ing artifacts are not necessarily the main contributor of false colo- calization results. Channel shiſts and crosstalk should definitely be considered when measuring colocalization (pixel or object based). Hot pixels/cold pixels, driſt during acquisition, chromatic aber- ration, and undersampling are also spoiling this type of analysis. With 100 nm TetraSpeck beads imaged with a confocal (36 x 36 x100 nm sampling), all 4 channels should overlap and give a Pear- son value that is close to ‘1’. However, we measure values far from that (even below <0.5). See the example image at the bottom of this webpage:
https://svi.nl/ColocalizationBasics. Tere are also animations on this same page and
https://svi.nl/BlurAndNoise- AffectColocalization that explain other imaging pitfalls as well. Vincent Schoonderwoert
vincent@svl.nl
Gold Palladium Target Composition Microscopy Listserver Dear all, what are the relative amounts of gold and palladium
in a Au/Pd target? I used one for the first time yesterday and was surprised to see a peak for Au, but not Pd in an EDS analysis. Tank you in advance. Stephane Nizet
nizets2@yahoo.com
In most cases the Au/PD ratio is 60/40. What kind of
EDS analysis did you do? Point scan? Maybe try an area scan. Rohan Prakash
rohan.prakash14@
gmail.com
It usually has 60% Au and 40% Pd. What else was in the sam-
ple? If there is another peak at around 21keV and 3keV, you would not be able to see a Pd peak. Sayit Uğurlu
sayitugurlu@gmail.com
Tis is a good example of when to use DTSA-II or Electron
Flight Simulator. Also, you want confirmation the 80/20 or 60/40 Au/Pd target is weight% and not atomic%. You did not mention your electron beam energy (KeV), substrate, or expected coating thickness. Jim Quinn
james.quinn@stonybrook.edu
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