There is a section on high and low molecular weight surfactant protein D from an publication by Grith Sorensen in Frontiers in Medicine, 2018 which has the following excerpt. “High-molecular weight SP-D multimers are only partly dependent on disulfide crosslinking of the N-termini, and a proportion of SP-D subunits are non-covalently associated. This allows interconversion between HMW SP-D and LMW SP-D trimers, as demonstrated using size permeation chromatography (36) (Figure 1B). The HMW/LMW ratio depends on the concentration of the protein in solution, with low-protein concentrations favoring the decomposition of multimers into trimers. In addition, the HMW/LMW ratio increases with affinity purification of SP-D, suggesting that ligand-binding facilitates assembly of SP-D trimers into multimers (Reference to an earlier article by the same author).”
There is specific reference to the ratio of high to low molecular weight multimers of surfactant protein D in relation to protein concentration (in the laboratory setting), and to the methionine 11 to threonine 11 allelic variants on the ratio of high to low molecular weight multimers of SP-D in humans.
It seems almost legitimate to view the two different peak plot patterns foud in the N termini peaks, traced from actual images of SP-D dodecamers (traced as two arms, i.e. hexamers – arm 1, and arm 2) found in the N termini of SP-D dodecamers. This valley seen about half the time in the center of grayscale N termini peaks (LUT tables traced in ImageJ) from AFM images (Arroyo et al, 2018) might suggest that even among dodecamers there can be both close tie between N termini (covalent links between two trimers) and loose associations, as well as a single peak, or two peaks respectively). In addition, the trace depends also on “where the segmented line is drawing during the trace, and the brightness saturation of the image.
This program was organized by Aaron Miller from online references to using Lag Threshold and Influence to detect peaks in signals. The signal here is the excel output from a plot of a surfactant protein D dodecamer (plot of a single hexamer, CRD to CRD shown here) which was subjected to L-5, T-1, I-0.01. The peaks are identified (black line series) while the actual plot is shown as the blue line. Using this csv export I added the peak widths and heights using CorelDRAW. I will convert height into grayscale, and width into nm. Itdid take several minutes to create the bar graph which has been colored in accordance with known, as well as yet unidentified peaks which I have consistently observed over many plots of nearly a hundred dodecamers of surfactant protein D.
1) pie in the sky purpose = adding this peak finding option to ImageJ (which someone else will have to do (LOL)).
2) select just a few of the image processing programs, filters and masks that are free, optimal, easy, and produce images that can be analyzed, and likewise, find signal processing programs that are free, easy to use and identify which settings produce the most useful data for statistical analysis of images obtained from microscopy.
CRD=carbohydrate recognition domain (orange); Neck domain (yellow); unknown, wide peak (white); unknown low and narrow peak (pink); unknown large relatively tall peak adjacent to the glycosylation peak(s) (dark green); glycosylation peak(s) (light green); unknown tiny peak between N termini peak and glycosylation peaks (purple); N termini peak(s) (peach). Actually the halves of the hexamers should be identical however, the artifacts that arise from processing (true of all microscopy) show that not all elements are present in all tracings. Eg, the neck domain is sometimes covered up by the CRD domain as the former is largely nested under each of the three globby CRD in each trimer. How I trace the segmented, 1px line over the image is hugely important, and aim for the brightest places along the length of the hexamer. (Image used for this plot has been shown on this site so many times that posting it again just wastes space (LOL)).
COMMING SOON: Are there instances where people can more accurately identify peaks than image and signal processing algorithms?
I am so frustrated with image and signal processing. I dont care what settings (threshold, smoothing, slope -?) are applied. When I see the results of peak finding tags a tiny peak (see red arrow on left)(not so say this isnt an important peak because i think it is — see orange vertical line under that peak) but ignoring a huge, easily seen, not to be overlooked peak massive peak (see red arrow on the right and peak with NO ORANGE line to the peak) I just dont trust any of it. I understand that slope and amplitude can be adjusted in these programs, but when upcoming and trailing values mess with “reality” (LOL).
COMMENT: this is a plot of a hexamer of surfactant protein D (CRD peaks are on each end, N termini junction is the center peak)
COMMENT: Just think… climate scientists and financial advisors are using similar algorithms to predict doom/prosperity.
Once to every man and nation,
Comes the moment to decide,
In the strife of truth with falsehood,
For the good or evil side;
Some great cause, God’s new Messiah,
Offering each the bloom or blight,
And the choice goes by forever,
‘twixt that darkness and that light.
– James Russell Lowell