Lysosome-containing LC3+ structures

Hi everyone,

I would like to use CP for calculating the amount of autophagosomes which are contained within lysosomes in cells treated with BfnA1. I have set-up this pipeline-just wondering whether it could work. Could anyone help me in this issue ? [nuclei (blue); Lysosomes (red); & autophagosomes (red)].

<img src="/uploads/cellprofiler/original/2X/2/2b526e2128252dadf94b17810c25aee8f105ada9.png"

Lys_pos_Lc3.cpproj (419.3 KB)

Once again, thank you very much for your helpful support.


The best way to find out is to try it out. But in order to help you with this issue, having .tif images of each channel individually would be helpful, as would a more specific idea of what you would like to measure.

If lysosomes and autophagosomes are both red, how do you plan on assessing them individually (in order to be able to detect their colocalisation)?

When speaking of the “amount”, do you mean a count of individual “spots” (i.e. organelles), or do you simply wish to assess the signal ratios (pixel intensities per cell and their ratios)?

Generally speaking, your images look fabulous! May I ask what specifically which antibodies you are using?

Hi Fabba123,

thanks for your prompt reply. There has been a mistake: the LC3+ organelles are in red and LAMP1+ organelles are in green. Regarding your questions, I would be able to count the LC3+ spots contained within LAMP1+ organelles… I have separated the merged picture and you have all the channels. Let me know if you need anything else…

Once again, thanks for helpful support, and my best wishes.


Ps: Both Lc3 and Lamp antobodies are commercially available. Let meknow if you need of more details.

I’d like to help, but I’m having trouble in a couple of places-

-Which of your split channels there is the green and which is the red? They’re really quite similar.

-What exactly are you wanting to quantify- the large spots that appear ~1 per every other cell, or the background distribution? The latter seems more like what I’ve seen from papers, but given the images you’ve uploaded I don’t think segmenting the cytoplasm into discrete objects will be possible. If it’s just the large spots, that may be doable.

-Are these the original images that came off your microscope? These seem like they may have been processed in some way, which’ll make them harder to analyzed. Do you have any unprocessed images?

Hi Beth,

thanks for your reply. Since the functional autophagy progression relies on autophagosome-lysosome fusion event, I would quantify how many LC3+ vesicles are within Lamp1+ organelles. To challenge this, I treated my cells with autophagy-flux inhibitor BafilomycinA1, which slows down the degradation of LC3+ autophagosomes by preventing lysosome degradative capacity. Of course, my goal is to quantify the amount of large spots which are positive for both LC3 (red) and LAMP1(green) organelles. Regarding your questions, this pictures came off directly from microscope but I can give you other unprocessed pictures (untreated and BfnA1-treated cells) where you can better appreciate such changes…

Once again, thank your very much for your helpful support and let me know if you need more details.

My Best,

Lamp_edits.cppipe (18.8 KB)
I’ve attached a rough edit that I think will do a better job of finding your objects and doing the measurements that you want, feel free to tinker with it more.
That being said, my $0.02 are that if these definitely are the raw full-sized images (most microscopes don’t export raw data as .png, it’s either as a .tif or a proprietary format like .nd2, .zsm, etc; the full field size is usually multiple of 64 (512, 1024, 2048)) and if there’s a way for you to go back and retake your images I think you should consider it- either with less binning or a higher resolution objective or something like that. Your objects are very close to your resolution limit, which with your background makes it tricky to pull out. Looking for objects with a feature size of 1 (as you had in your original pipeline) is dangerous, because it can pick up any piece of noise or junk around.
Good luck!

Hi Beth,
many thanks for helpful suggestions :slightly_smiling:… I’ll go back to microscope and retake pictures at high resolution-as you said. Once again, thank you very much for your help.