Living Systems Institute

Solana Group

Stem Cell Biology and Evolution

All multicellular organisms have pluripotent cells that can differentiate into multiple cell types. In animals, these are present in embryos but sometimes as well in the adult stages. Often this happens in animals that can regenerate and even use this capacity for asexual reproduction. Our group is interested in stem cells, pluripotency, differentiation, and their evolution across the animal tree of life. There are several conserved features in invertebrate stem cells, including RNA binding proteins and epigenetic regulators. However, in most animal groups the identity and properties of their stem cells remain unclear, as their study has been challenging for decades. Recently, single cell analysis has changed this. We have used single cell transcriptomics (scRNA-seq) to profile stem cell differentiation trajectories in the planarian Schmidtea mediterranea. Now, we aim at using single cell analysis across the animal tree of life to decode the basic principles of animal stem cells and regeneration.

Our methodology


We have developed a novel cell dissociation approach (ACME, García-Castro et al. Genome Biology) which fixes the cells while they are being dissociated, preventing the stress imposed by enzymatic live dissociation. ACME is very versatile and can be used in different species. We have also optimised a modified version of SPLiT-seq, a scRNA-seq technique that uses combinatorial barcoding. SPLiT-seq is cheaper than droplet methods, can be scaled up and allows multiplexing (avoiding batch effects). This enables us to perform functional genomics comparisons with cell type resolution. Our group uses the planarian model as a workhorse to develop new single cell methods to decode stem cell and differentiation properties, which can be later used in other animal stem cell models.

ACME Dissociation (left) + SPLiT-seq (right): We have developed a novel approach for single cell transcriptomics that fixes the cells from the very beginning (ACME) and combined this with SPLiT-seq, a novel combinatorial barcoding method of single-cell transcriptomics that allows sequencing >50K cells in one run (Garcia-Castro et al. Genome Biology 2021).

Studying stem cells functionally to learn their regulatory code

The high multiplexing capacity of SPLiT-seq solves the problem of multisample experiments with single cell resolution. Our current technology can multiplex 2-24 samples in a single SPLiT-seq experiment, and we are upscaling this. This reduces batch effects, eliminating the need for integration approaches and dramatically reduces the price. For instance, we have used this multiplexing capacity to study cell type allometry using single cell transcriptomics – (Emili et al, bioRxiv 2023). By comparing planarians of different sizes we have measured changes in cell type composition, gene expression and cell size. We are currently upscaling this to study the role of transcription and epigenetic factors in stem cells and differentiation, as well as the molecular and cellular detail of planarian regeneration.

Studying gene regulation with single cell resolution: Multiplexed approaches to single cell transcriptomics such as SPLiT-seq allow performing differential gene expression analyses with cell type resolution to investigate the role of individual genes in individual cell types.

Stem cell biology and regeneration across the animal tree of life

We aim at using methods developed in S. mediterranea to other regenerating organisms across the animal tree of life, to understand the cellular and molecular principles of stem cells and regeneration. Our laboratory has already generated a cell type atlas of the regenerating annelid Pristina leidyi and is generating cell type atlases of the cnidarian Hydractinia symbiolongicarpus (in collaboration with Uri Frank, Galway) and the colonial ascidian Botryllus schlosseri (with Lucia Manni, Padova). We are under way of obtaining other regenerating species. The long-term goal of our research group is to apply single cell methods to diverse regenerating species.

Current PhD studentships offered

Developing graph mathematics methods to study cell type evolution by single cell transcriptomics

Cell types are the basic units of multicellular organisms and play a key role in understanding biological systems. While some cell types, like neurons and muscle cells, are found across various animal groups, others, such as vertebrate immune cells or cnidarian cnidocytes, are unique to specific lineages. This raises fundamental questions: How did this diversity of cell types evolve? Are new cell types frequently generated during evolution, or are they largely conserved across different groups? Understanding this is essential for unravelling the evolution of cell types and, by extension, multicellular life.
Recent advances in single-cell technologies have allowed us to investigate cell types in great detail within species, but comparing cell types across species remains difficult. The evolution of cell types is closely linked to the history of each gene and its expression patterns. Yet, there is a lack of methods that combine single-cell analyses with comparative genomics to uncover these evolutionary connections.
Our project aims to develop graph mathematics methods to compare cell types, using single cell transcriptomic data. We will create novel algorithms and data analysis tools that enhance existing computational methods to trace the evolutionary history of genes and their associated cell type expression patterns. To test our hypotheses, we will generate single-cell data from key evolutionary lineages. Moreover, we will leverage advanced artificial intelligence and machine learning techniques to classify genes based on common expression profiles. The project also has an important component of wet-lab data generation, including single cell transcriptomic experiments using a variation of our SPLiT-seq technique.
Understanding how cell types evolve is a complex yet vital area of research. This project aims to tackle key questions surrounding cell type evolution in multicellular organisms, providing insights that will have wide-reaching implications across fields such as developmental and evolutionary biology.
The supervisory team combines expertise in wet lab single-cell transcriptomics and bioinformatics. The project offers training in cutting-edge single-cell transcriptomics techniques (Jordi Solana) and advanced computational tools, including network mathematics (Piotr Słowiński / Magda Strauss).

Reconstructing ancestral animal cell types by a single cell analysis and comparative genomic approach

Cell types are the fundamental building blocks of multicellular organisms and are central to understanding the biosciences. While many cell types, such as neurons and muscle cells, are shared across animal lineages, others appear specific to certain groups, like vertebrate immune cells or cnidarian cnidocytes. This raises important questions: How did this vast diversity of cell types emerge? Are new cell types frequently created during evolution, or are they conserved across groups? Answering these questions is crucial for understanding the evolution of cell types and, consequently, multicellular organisms.

Recent advances in single-cell technologies have enabled the detailed investigation of cell types within species, yet comparing these cell types across species remains challenging. The evolution of cell types is deeply tied to the genomic history of each gene and its expression patterns. However, there is a lack of methods that integrate single-cell analyses with comparative genomics comparisons to reveal these evolutionary relationships.

Our project aims to reconstruct the ancestral cell types and their evolution within an animal group by combining single-cell analysis with phylogenomic data. We will develop innovative algorithms and data analysis tools that build upon established computational methods to map the evolutionary history of genes and their associated cell type expression patterns. To validate our hypotheses, we will generate single-cell data from key evolutionary nodes. Additionally, we will incorporate advanced artificial intelligence and machine learning techniques to classify genes based on shared expression profiles.

The tools and algorithms developed will allow us to trace the evolutionary trajectory of each cell type and reconstruct the ancestral cell types within the chosen animal group, Platyhelminthes. This group is particularly suitable due to its well-studied cell types and evolutionary history. Moreover, Platyhelminthes, such as planarians, possess stem cells that continuously differentiate into all cell types, providing a unique opportunity to profile the entire cell type repertoire from adult samples.

Understanding the evolution of cell types is a critical yet enigmatic area of study. This project will address fundamental questions, shedding light on cell type evolution in multicellular organisms and offering insights valuable to a broad range of fields, from developmental biology to evolutionary biology.