There is a dilemma for researchers in terms of their choice of image processing programs. The industry giants charge considerable amounts (matlab is exorbitant), what used to be high prices for CDs but now for online subscriptions. Does the US taxpayer need to pay for these services – it doesn’t seem quite right that we pay industry graphics prices to do science which benefits all of humanity (hopefully).
I have spent the better part of the year (not full time but avidly) trying to figure out who has the image processing program that is the easiest to use (i am talking here about real ease of use…. as I come to this field as a visual learner, without apologies, but find much lacking in the so called “intuitiveness” of many programs). I am also interested in the least expensive program as i dont want to feed the giants in the commercial world. Another goal is to see whether all the bells and whistles that are touted in the new image processing programs really make a difference for scientists who are wanting to do some relatively simple things…. get rid of dirt, get rid of pixels, identify and enhance hard to see details, to put statistics ahead of guessing about shapes, peaks and the like.
I began 50 years ago with wet-darkroom prints, and the simplest tablet and stylus, haha i don’t even remember the name of the setup but could look that up in a materials and methods published somewhere. I used camera lucidas and pencil, then digital images. The earliest processing was just a hand waving (dodging and burning) then using those same terminologies, photoshop appears.
The earliest plates for publication were made on matboard with a drymount press and tissue. The earliest program that i used for making images into figures was the first version of CorelDRAW (it was version 3) cracked on 11 pink 3.25 inch floppy discs. These were mostly plots and line drawings.
50 years later, I still use CorelDRAW (x5 and 19), CorelDRAW Photopaint (x5 and 19) and still use Photoshop (6 and 2021), and have in the past year examined ImageJ, Gwyddion, and GIMP. It is difficult not to be biased because CorelDRAW has had both raster and vector elements that in my way of thinking are more than adequate for both processing images, measuring features, and creating diagrams. CorelDRAW has been nicely compatible with Photoshop and Illustrator (though the reverse has never been as smooth). I have used images with 100 layers or more imported into CorelDRAW from Photoshop with ease.
So the purpose of this is to determine whether it is possible to do image processing with the same filters and measuring options in the open source software as is done with the high priced industry standards.
The test today is the unsharp mask filter in GIMP. I do like the ease with which the sliders can be manipulated with instant preview updates…. i don’t much like the actual “typing” from the number pad into the slider. But using the surfactant protein D images (that I have used from published papers (in this case those shown here are some good and not so good images from Arroyo et al (AFM of surfactant protein D). Regardless of the original image quality, GIMP (open source image processing software) does a great job of masking.
Pay special attention to the small masked areas in the collagen like domain of each of the SP-D trimers…. yep…. 3 to 4 small peaks beside the glycosylation peak, especially nice in lower right image, and in that one and the lower left image… a tiny peak on the side of the N termini peaks is a new little peak (of which i am trying to determine the relative incidence). Error bars are what i have used for measurments and are visible, most are 100nm but the tiny one is 20nm, and lower center has no error bar.
Determining a beginning and an end creates a dilemma for measuring LUT plots. There are many variables playing on where to start the plot-line and where to end it. The total distance from one end of the CRD of a hexamer to the other CRD can be measured in at least these ways. 1. brightest spot to brightest spot, 2. straightest line (whether that means beginning or ending the line in a place where one of the three elements of the CRD are spread apart creating a darker and shorter line path.
Yep…. coronavirus globe glass and polymer clay ornaments that are made to be accurate representations of the surface look of the corona virus. Just for fun and remembering the headaches of this particular pandemic. Order from the website here.
DMBT1 is an interesting multimer and it according to a couple of images I have seen it can appear in two configurations: ring and linear. One confusing issue is why, if the linear molecule has arms apparently extending in two directions along the spine, does the ring multimer not have arms extending toward the center of the ring?
There is a very simple explanation, which I am proposing, not as someone who knows much about DMBT1, but from the perspective of someone who has done lots of imaging, and microscopy and has found that there is an abundance of information in images, it just takes some serious concentration and visualization.
So for starters here are two diagrams (not to scale, not depicting the correct number of SRCR- like domains because I dont really know that number but suspect from looking at AFM images that it is more than 8) and not suggesting that there is a given number of arms in a ring or linear multimer (though the two images I have examined have about 30+ arms each), and I am not suggesting size of the individual proteins modeled nor the amount of space in the link regions (SIR) is accurate, but just making a suggestion about how the ring and linear multimers might actually NOT be so different. At the same time, it eliminates the need for explaining why the linear multimer has arms on both sides and the ring multimer does not.
Below: ring multimer (left) linear multimer (right). If one looks closely at the right image one sees that there is a red fill down the center of the line, which actually I am suggesting represents a collapse of the open circle on the left…. literally stuck together obliterating the space in the ring. This possibility originates from a shadowed image in a publication by Erica Crouch where she attributes the image to John Heuser. The spine of that linear multimer obviously (at least to me) is a junction of two rows.
I suspect that the SRCR domains have the ability to be closely bound, side to side, maybe two or three (judging by the height and width of brightness peaks seen with AFM). If they bind side to side, then why not also bind to molecules across the diameter, more side to side associations, to close the circle. This would look a little more like the sideview of a lampshade with the spine only raised slightly.
Having the arms on either side of the ring multimer is visually untenable, my guess is that it is an easy leap to suggest that it is not tenable in the molecular sense either. See diagram below. Flattening the ring is about the simplest explanation to explain the side to side arms extending in the linear multimer but not in the ring multimer. See diagram below of the “not really plausible” ring multimer.
For these diagrams, the model for the individual arms was redrawn from a diagram in a publication by Martin P Reichhardt et al, Structures of SALSA/DMBT1 SRCR domains reveal the conserved ligand-binding mechanism of the ancient SRCR fold.http://doi.org/10.26508/lsa.201900502.
The diagram above (with questionmark) is about the length of each arm that I measured in ImageJ so to make it more or less a reasonable estimate, some of the SRCR domains are not shown (which is consistent with often occurring different numbers of repeats). It seems to me that the linear structure must have some kind of end to end, as well as side to side binding in SRCR domains at the N termini. Just thinking outloud, suggestions are welcome.
The complexities of biology, physics, math, etc may never be completely understood — thus it is better to have questions that cannot be answered than answers that cannot be questioned.
modified from an original, author of that one, unknown — mm 10 10 2020
Finding ways to quantify shape, peak heights, widths, and patterns in a molecule which has long arms (100nm+) and lots of flexibility therein has its challenges. PLEASE READ< THIS IS NOT SURFACTANT PROTEIN D and I was NOT prepared by me, but will remain anonymous for the time being. BUT, here is a section of a molecule which has approximately 30 arms with very definite “beads on a string” appearance. Not all of the arms could be traced with sufficient (squinting and mental debating) could be deciphered (in fact relatively few remain untangled enough to produce a LUT plot with any kind of predictability. Here is a stack, which facilitates looking at the arm, the actual ImageJ tracing and the plot of luminance (brighness, — i havn’t figured out which name is the best yet). Cropped and rotated (so that the bright peak is always at the left — and this was the beginning point of the traces, thus the biggest peak is also always at the left) arms are aligned in a column and are all the same magnification and enlargement. The plots at the right are given in the same height as ImageJ scale measured them, but were aligned by nm (100nm) which was a mean length of a trace of several of the straightest and most easily traced arms. No other alignment of the individual peaks or plots was made but picking a “last” peak that is commonly occurring and normalizing that with the beginning of the first peak cold make the interim peaks more easily seen. Hopefully the technique here will prove helpful looking at SP-D fuzzyballs.
There is pretty much a vertical line for a very wide and prominent ‘First peak’ (blue) followed by a regular less prominent peak of lesser height (gray). Many of the arms showed four peaks before a rise to the second tallest middle peak (yellow). Two subsequent peaks show up consistently (green and purple) moving to the right. (TOP IMAGE). Five of the easiest arms to plot also have peaks that line up well (BOTTOM IMAGE panel of 5 arms). (the image used was processed as 2DFFT in gwyddion then opened in ImageJ, and traced with a segmented line)
I do not have a clue what 2D autocorrelation does in Gwyddion (yet) but it is so odd that it produced this image of an SP-D multimer. I see the N termini junction, and a concentric ring which is only very faintly visible in the original, and around that ring lateral lines with four blips. haha. but no CRD. Any clues.