Category Archives: Ultimate order, the cell

The beauty and order of life is astounding.

Peak counts of a single surfactant protein D molecule (an AFM image): Possible peak in the center of the N term peak?

Only 18,  times out of 636 plots of surfactant protein D trimers was there something that could be identified as a peak at the top center (or close to center) of the N termini junction peak (light green in the two top plots below).  This was found in 3 ipeak.m signal processing settings, and one Lag-Threshold-Influence algorithms used in a batchprocess (also signal processing), and the rest of the time with counts of plots that had been image processed.  One of the more prominent displays of that peak came from Gwyddion, gaussian blur and limitrange 100-255 filtering.

The bottom plot below (Gwyddion, gaussian blur and limitrange filters) was one of the two plots given to the citizen scientists from which to count peaks.  None of those individuals chose to identify this thin N termini blip as an actuall PEAK.  It clearly exists sometimes, but its relevance is not known.

The division of the N termini peak into TWO clearly identified peaks (rather than three which would include the central “green” peak below) was more common.

Batch process peak detection using Lag, Threshold, Influence

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.

PURPOSE:

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)).

 

plot of grayscale peaks found along a hexamer of surfactant protein D

COMMING SOON: Are there instances where people can more accurately identify peaks than image and signal processing algorithms?

More comparison plots for a single arm of a multimer

I think this image shows pretty nicely how little, except for cosmetics, the image processing of a cropped portion of a multimer involved in immune defense and differentiation in epithelia shows here affects the raw data (LUT grayscale plots).  There are small variations in the length of the “arm” which are more likely due to that actual tracing in ImageJ than any difference due to processing. That said, gaussian blur tends to increase the dimensions slightly, highpass filtering increases the grayscale, and unsharp mask creates greater peak heights.  So the peak number and the smoothness of the plot can be improved with processing, and the peak height, but no real gain in information is obtained.

The greater task is to look carefully at the molecule, deciding whether tracings of the beginning and ending of close, twisted and overlapping arms (as plotted) (made by hand) make sense.

Images below show plots (color coded as to image processing filters used) and the actual images and trace-lines (screen print from ImageJ tracing). In this case: filters, each named,  were performed in photoshop; plots were made in excel as traced in ImageJ; calculator.net was used for determining means and SD of arm length and peak number;  corelDRAW was used to normalize the x axis (y axis remained unchanged from original plots) and to color code the plot lines.


Next task is to measure peak widths and heights. Unsharp mask (amount 300, 40px radius, 20 threshold) followed by a 10px gaussian blur provided the smoothest plot.  The High peak (left) and low peak height for the next three peaks, and a subsequent high peak seems to be a pretty prevalent pattern for arms in this molecule.

This is the plot for unsharp mask and gaussian blur. It is so remarkably like the very first plots I made on this protein.  Since 99.34 nm was close to 100 i used a 100 set of 1 nm dividers to mark out the valleys of these peaks.  Interesting features are the absolute regularity of the first five peaks, not in height, but in width, and the first and 4th peaks being higher, and the series of similar size peaks in the middle of the plot.

 

Highpass filter in photoshop (250px radius)

High pass filtering can help sort out background from data, especially microscopic data. Here is a summary about the high pass filter when used alone, and with gaussian blur. The subject matter is a segment of a multimer (either round or linear) and the brightest peak is where the tracing for gray scale LUT tables begins.  Each trace is shown, and along with that a plot of the peak height (and soon to be measured peak width (at mid height and at the base).   The plots of the arms shown at the bottom of the figure are color coded with the plots shown in the graph (graph made in image J, highpass filter and gaussian blur made with photoshop 6. Figure assembeled in CorelDRAW x5. THis is NOT surfactant protein A or D, but another multimer involved in innate immunity.

When the “political wish” becomes a “rosary”

Has anyone else noticed a speech pattern from the previous president.  It is like a set of words become a mantra, perhaps a meditative experience, or a soothing mantra, maybe even a prayer list, a mandala, a rosary or prayer beads. The following quote made me think of this.

This is the quote “”The county has, for whatever reason, also refused to produce the network routers. We want the routers, Sonny, Wendy, we got to get those routers, please. The routers. Come on, Kelly, we can get those routers. Those routers. You know what? We’re so beyond the routers, there’s so many fraudulent votes without the routers. But if you got those routers, what that will show, and they don’t want to give up the routers. They don’t want to give them. They are fighting like hell. Why are these commissioners fighting not to give the routers?”

Verge of a Dream: No more than one clear minute

I think all will
again be well when
the garden begun
before unforcasted
change have you
Back again to then
connect
The dreams of your
Father and talents
of the family.
I think that the
direction can be changed
releasing in
centrifugal loss
that not needed,
to become more
like Saturn, in
a ring about the planet.
I think you will,
once the swirling air
settles, know
why you were called
then, for no more
than one clear minute.
And that moment
Is maybe more but
no less than
nights, than mornings
and in between,
spent in the
eye of the hurricane,
grasping for an
answer to
hold on to.

RLB 16/19/2021

GIMP for processing AFM images – unsharp mask

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

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.