PCOMPBIOL-D-26-00610

SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification

PLOS Computational Biology

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Daniele Marinazzo

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PLOS Computational Biology

Additional Editor Comments:

Both reviewers viewed this submission positively, highlighting the tool development nature, and suggested minor revisions, all of which can be straightforwardly addressed by the authors.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This review is uploaded as an attachment

Reviewer #2: This is a nicely performed and well written study that develops a novel tool for the study of brain circuits. Specifically, they use machine learning to develop a pipeline for the detection and analysis of synaptic proteins. The approach is well documented and importantly they have attempted to train and test their model on different types of data. This is a big plus as typically these tools are developed to analyse a quite narrow and specific type of data and consequently lack generalizability. Overall, the study and associated tools will be very useful for the field and I support publication following the corrections highlighted below.

Major comment

Fig. S4 and more generally. Were Bassoon puncta detected as reliably as PSD95 given model training mostly focused on the latter. Would be useful to better highlight and quantify utility of the method across labelled proteins. Although they have used different sources of training data, most of the training and (and therefore I assume testing) data is PSD95. Consequently, it is important to know if there is greater error in the non-PSD95 test images, or a uniform accuracy regardless of staining. They state in line 341 that their view is that sample prep is bigger source of variability and so method should be generalizable, but it would be nice to see evidence for this claim.

Figure 5. Where on the dendrite were images taken? There is evidence that synapse density onto PV cells drops as you move further from the soma (e.g Morabito et al., Neuron 2025). How did they account for this effect and (ideally) can they replicate it? They state that size and length of dendrites not different, but where on the dendrite might also be important.

Additional analysis – not essential but very useful and enhances impact

Figure 5 and S6. Related to the point above and the analysis of PSD95 clusters. It seems that there is a reasonable amount of PV variability within a dendrite as well as between dendrites/animals. If they were to trace along the dendrite and plot PSD95 density and intensity as a function of PV intensity, do they co-vary? Given PV is a calcium buffer you could imagine it might be biased towards regions of high synapse density or stronger synapses and therefore calcium. This would be an interesting finding that could enhance the impact of the study.

Minor comments

To improve the ease of interpretation, it would be helpful if they could explain what the computer vision metrics (IoU etc) means in the context of their experiments and how to interpret the graphs in F1 for non-experts.

Fig 1 legend does not match the figure. Some panels missing.

Figure S4 and associated text. How were dendrites determined? While I don’t doubt most synapses were aspiny, unless you can see parent dendrites then it is hard to assess this.

Line 225. Wording. I would suggest density of protein, or intensity of staining but not intensity of protein. Also, they mention intensity in text but plot size. Could be helpful to explain how the two are related in small structures such as synapses. Also, in the legend need to explain how size is calculated.

Line 297 and 305. Do they mean Fig S6?

Line 329 missing of before curated

In methods they mention 4 human experts, yet figures show 5 annotators.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: PCOMPBIOL-D-26-00610_Review.docx

Dear Dr Bygrave,

We are pleased to inform you that your manuscript 'SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

William Redman

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

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The authors have sufficiently addressed (and, in my opinion, went above and beyond to address) all the concerns of the two reviewers. These revisions have greatly strengthened the work and I believe it is ready for publication.