At the annual Human Proteome Organization (HUPO) meeting in Toronto, 25 HPA-affiliated members contributed more than 25 talks and posters across sessions. Among them was Loren Méar, researcher in the HPA and a postdoc working both in Pauliina Damdimopoulou's group at Karolinska Institutet and Cecilia Lindskog's lab at Uppsala University. Loren was selected as a poster competition finalist for her work "Spatial proteomics uncovers structural and molecular complexity of the human ovary in 2D and 3D."

In simple terms, spatial proteomics means not only seeing which proteins are present in a tissue, but also exactly where in the tissue they are. In a conversation, Loren explains how her team is building a detailed 2D and 3D map of the human ovary, why this is so challenging, and how a panel of Human Protein Atlas antibodies from Atlas Antibodies is helping reveal completely new aspects of ovarian biology.

Loren, congratulations on being a finalist in the poster competition! In short, what is this poster about?

This project is really about getting a deeper understanding of the human ovary. In 2025 we still have almost no idea of its spatial organization not only in 2D but also in 3D. To tackle this, we are using a combination of different proteomics approaches: multiplex immunohistochemistry (mIHC) and deep visual proteomics (DVP).

Being affiliated with the Human Protein Atlas has given me access to extensive antibody data across many tissues, which has been incredibly valuable.

For this project, you developed a panel of highly validated Human Protein Atlas antibodies from Atlas Antibodies, targeting different cell types in the ovary. Could you elaborate on the choice of method?

Recently, colleagues in my team at Karolinska Institute showed, at the transcriptomic level, that there may be two types of follicles (the egg cell-containing structures). So our idea was to see whether we could confirm these findings at the proteomics approach.

One challenge is that the Human Protein Atlas doesn't always include follicles or oocytes in their ovarian samples, so we needed extra validation. That's why most antibodies in our panel come from Atlas Antibodies: we wanted strong, reliable tools.

This is where multiplex IHC becomes a major upgrade: by combining antibodies in specific ways, we can precisely target different cell types and still leave an "empty slot" for testing additional proteins if needed. In this case, we used a cocktail of antibodies to clearly distinguish between these two types of follicles, and actually we were able to do that.

We developed and validated the panel using the Opal system, and now we have a robust method to visualize ovarian structures across different ages, conditions, and disease stages.

What would be a meaningful outcome for this research?

We are using this fixed panel in different projects, always related to the ovary. For example, we are trying to understand what happens during puberty: what changes occur, and what drives oocyte maturation.

We're also looking at how other features, like blood vessels or neurofilaments, behave. Having a reliable method allows us to identify potential markers of tissue maturity.

We also work with tissue from children undergoing fertility preservation. These patients often have cancer or severe diseases and may receive treatments that affect the ovary. With our approach, we can assess whether the ovary, and especially the oocytes, look healthy enough to preserve and eventually reimplant.

If we can identify good markers and validate them with multiplex IHC, this could eventually be translated into a clinical workflow using classical IHC to evaluate oocyte maturity or overall ovarian health.

What do you take home from this poster session?

It was honestly a fantastic experience. I was only supposed to stay by my poster for an hour, but I ended up staying more than two hours, people kept coming, asking questions, and discussing the staining in depth.

Multiplex IHC feels a bit "vintage" in a way, but it's still super cool and surprisingly high-throughput. It also proves that sometimes, with less, you can actually generate more meaningful data. I really think this type of approach has a place in the future.

I was also struck by how many people aren't familiar with what the human ovary actually looks like. Many assumed it's packed full of oocytes, which is far from true. So the session turned into a lot of biology teaching, something I really enjoyed.

And of course, being selected as a finalist was a proud moment. This is a topic that isn't often explored in this way, so it felt really meaningful to bring attention to it.