Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Nikolai Hecker , Niklas Kempynck , David Mauduit, Darina Abaffyová, Roel Vandepoel, Sam Dieltiens, Ioannis Sarropoulos, Carmen Bravo González-Blas , Elke Leysen, Rani Moors, Gert Hulselmans, Lynette Lim, Joris de Wit, Valerie Christiaens, Suresh Poovathingal, Stein Aerts

Posted on: 2 July 2024 , updated on: 3 July 2024

Preprint posted on 18 April 2024

Pioneering multi-omics and deep learning models of the chicken brain shed light on amniote evolution.

Selected by Rodrigo Senovilla-Ganzo

Categories: bioinformatics, evolutionary biology, genetics, genomics, neuroscience

Why I chose this preprint:

For me, there were two main reasons to select this preprint: Firstly, because it is one of the first atlases of chicken pallium, together with the ones presented in the simultaneously published preprints of Zaremba et al., (2024) et al. and Rueda-Alaña et al., (2024). The multi-omic atlas presented in the preprint highlighted here increases our knowledge of the avian taxa, sharpening the debate surrounding amniote pallium evolution and neuron type homologies. Secondly, and not less importantly, this preprint pioneers the use of deep learning and scATAC-seq methods to allow inter-species comparisons at the cell type level. These tools may become an incredible resource for the community of computational evolutionary biologists. Altogether, disruptive science and method development in one preprint, how not to highlight it?

Background:

Although the mammalian and avian brains both display high cognitive functions (Güntürkün & Bugnyar, 2016) and share common structures such as the telencephalon (Puelles et al., 2013), their subdivision, circuitry and functionalization have evolutionary diverged (Medina et al., 2021).

In both taxa, the telencephalon is divided in a dorsal telencephalon or pallium, and ventral telencephalon or subpallium. The mammalian pallium is mainly composed of the highly-specialized, layered neocortex. However, there are other pallial structures such as the amygdala, hippocampus, olfactory cortex, among others (Molnár et al., 2014). Meanwhile, the avian pallium is not layered, but organised in nuclei. Most of the avian pallium is formed by the dorsal ventricular ridge (DVR) (subdivided in mesopallium, nidopallium, entopallium and arcopallium), but there are also other important nuclei like the hyperpallium and the medial pallium (Reiner, 2005).

Establishing homologue cell types in the amniote pallium has been an arduous task. Some authors have claimed that homology is indicated by shared developmental fields (Puelles et al., 2017; Tosches et al., 2018; Colquitt et al., 2021), while others considered that shared adult gene expression and connectomics (Briscoe & Ragsdale, 2018; Stacho et al., 2020). The atlas and inter-species comparisons in this preprint add a new perspective based on enhancers and cutting-edge deep learning models.

Key findings:

Innovative approaches for computing cell type similarities.

In the preprint, Hecker,Kempynck and colleagues combined four approaches for computing cell type similarities: (i) transcriptome comparison, (ii) predictions from sequence-based deep learning models, (iii) correlations of derived nucleotide contribution scores, and (iv) similarities of transcription factor binding sites (TFBS) motifs.

For the transcriptome similarity analysis (i), the authors employed established methods such as SAMap and gene correlation. Comparisons at the sequence level (scATAC-seq) were carried out by a new deep learning tool (ii). A model was trained for each species studied (human, mouse and chicken) and used to compare or map the cell types to other species. This powerful tool was able to reproduce the transcriptomic results, even when it was only trained on the data from other species.

Figure 2. Deep learning models of pallium enhancers. Pipeline of the deep learning model training at the top, interspecies comparisons at the bottom left and example of Csfr1 gene expression and enhancers at the bottom right (Fig.3 A, B and C in Hecker and Kempynck et al, 2024).

This method was complemented with nucleotide contribution scores (iii), gradient based contribution scores and scores derived from in silico mutagenesis, which were confirmed by in vitro mutagenesis experiments. These experiments showed that mutagenesis of enhancers present in mouse could modify the enhancer activity of a mouse microglia cell line.

Lastly, a pipeline of different tools (TF-MoDISco, MEME suite, cisTarget) allowed the research team to extract group motifs from cell type specific differentially accessible regions (DARs). The correlation of these motifs’ presence also confirmed the ability to compute cell type similarities among the included species.

Multi-omic atlas of the chicken brain

This preprint presents one of the first atlases of the chicken pallium. The authors managed to annotate general cell types based on gene expression and further confirmed their identity through spatial Stereo-seq. The general non-neuronal cell types existing in mammals are also present in chicken: one-to-one pairs could be established in the comparisons. Similarly, GABAergic neurons (both pallial and striatal) also matched their likely homologue between avian and mammalian cell types. Nonetheless, scATAC-seq data seemed to cluster VIP and LAMP5, as well as SST and PVALB interneurons together in pairs. On the other hand, amniote equivalencies were not straightforward for glutamatergic neurons.

Conservation of amniote cell types

Figure 2. Cell type conservation of the chicken cell types. Marker genes for every cell type on the left, SAMap mapping scores on the right between mouse and chicken (Fig. 2 B and C in Hecker and Kempynck et al, 2024).

The main result of this study is the high conservation of general cell types despite the high evolutionary distance of amniote species. Non-neuronal, glutamatergic and GABAergic neuronal classes could be identified as homologous cell types by the four complementary computational methods described in the preprint. However, there is no such conservation among the subtypes of these different cell types.

The authors report a high conservation of the non-neuronal cell types. However, as there is no further sub-clustering, it was difficult to assess the conservation of their subtypes. In the case of GABAergic neurons, subtypes were shown to be highly conserved using the four methods, but the VIP/LAMP5 and SST/PVALB pairs were indistinguishable with the sequence-based approaches. Thus, there must be a shared regulatory machinery between these cell types, which is conserved in amniotes.

Among all cell types, it proved to be most difficult to find homologies for glutamatergic neurons. Despite the similarities among glutamatergic cells – as a general cell type – across amniotes, their subtypes display low similarity, especially compared to GABAergic neurons’ scores. The most robust similarities were found for medial pallium and mesopallium. On the one hand, the medial pallium clusters showed high transcriptomic and sequence-level cell type similarity with all methods used in this study, showing a high conservation of hippocampal cells between mammals and birds. On the other hand, the mesopallium clusters seemed to be similar to deep layers of neocortex, which was highly surprising. If confirmed, this similarity would require a new model to explain avian evolution.

Bibliography.

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Butler, A. B., Reiner, A., & Karten, H. J. (2011). Evolution of the amniote pallium and the origins of mammalian neocortex. Annals of the New York Academy of Sciences, 1225(1), 14. https://doi.org/10.1111/J.1749-6632.2011.06006.X

Colquitt, B. M., Merullo, D. P., Konopka, G., Roberts, T. F., & Brainard, M. S. (2021). Cellular transcriptomics reveals evolutionary identities of songbird vocal circuits. Science, 371(6530). https://doi.org/10.1126/science.abd9704

Gedman, G., Haase, B., Durieux, G., Biegler, M. T., Fedrigo, O., & Jarvis, E. D. (2021). As above, so below: Whole transcriptome profiling demonstrates strong molecular similarities between avian dorsal and ventral pallial subdivisions. The Journal of Comparative Neurology, 529(12), 3222–3246. https://doi.org/10.1002/CNE.25159

Güntürkün, O., & Bugnyar, T. (2016). Cognition without Cortex. Trends in Cognitive Sciences, 20(4), 291–303. https://doi.org/10.1016/J.TICS.2016.02.001

Medina, L., Abellán, A., & Desfilis, E. (2021). Evolving Views on the Pallium. Brain Behavior and Evolution, 96(4–6), 181–199. https://doi.org/10.1159/000519260

Molnár, Z., Kaas, J. H., De Carlos, J. A., Hevner, R. F., Lein, E., & Němec, P. (2014). Evolution and development of the mammalian cerebral cortex. Brain, Behavior and Evolution, 83(2), 126. https://doi.org/10.1159/000357753

Puelles, L., Harrison, M., Paxinos, G., & Watson, C. (2013). A developmental ontology for the mammalian brain based on the prosomeric model. Trends in Neurosciences, 36(10), 570–578. https://doi.org/10.1016/j.tins.2013.06.004

Puelles, L., Sandoval, J. E., Ayad, A., del Corral, R., Alonso, A., Ferran, J. L., & Martínez-de-la-Torre, M. (2017). The Pallium in Reptiles and Birds in the Light of the Updated Tetrapartite Pallium Model. Evolution of Nervous Systems, 1–4, 519–555. https://doi.org/10.1016/B978-0-12-804042-3.00014-2

Reiner, A. (2005). A new avian brain nomenclature: why, how and what. Brain Research Bulletin, 66(4–6), 317–331. https://doi.org/10.1016/J.BRAINRESBULL.2005.05.007

Rueda-Alaña, E., Senovilla-Ganzo, R., Grillo, M., Vázquez, E., Marco-Salas, S., Gallego-Flores, T., Ftara, A., Escobar, L., Benguría, A., Quintas-Gorozarri, A., Dopazo, A., Rábano, M., dM Vivanco, M., María Aransay, A., Garrigos, D., Toval, Á., Luis Ferrán, J., Nilsson, M., Manuel Encinas, J., … García-Moreno, F. (2024). Evolutionary convergence of sensory circuits in the pallium of amniotes. BioRxiv. https://doi.org/10.1101/2024.04.30.591819

Song, Y., Miao, Z., Brazma, A., & Papatheodorou, I. (2023). Benchmarking strategies for cross-species integration of single-cell RNA sequencing data. Nature Communications 2023 14:1, 14(1), 1–17. https://doi.org/10.1038/s41467-023-41855-w

Stacho, M., Herold, C., Rook, N., Wagner, H., Axer, M., Amunts, K., & Güntürkün, O. (2020). A cortex-like canonical circuit in the avian forebrain. Science (New York, N.Y.), 369(6511). https://doi.org/10.1126/SCIENCE.ABC5534

Tosches, M. A., Yamawaki, T. M., Naumann, R. K., Jacobi, A. A., Tushev, G., & Laurent, G. (2018). Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science, 360(6391), 881–888. https://doi.org/10.1126/science.aar4237

Zaremba, B., Fallahshahroudi, A., Schneider, C., Schmidt, J., Sarropoulos, I., Leushkin, E., Berki, B., Poucke, E. Van, Jensen, P., Senovilla-Ganzo, R., Hervas-Sotomayor, F., Trost, N., Lamanna, F., Sepp, M., García-Moreno, F., & Kaessmann, H. (2024). Developmental origins and evolution of pallial cell types and structures in birds. BioRxiv, 2024.04.30.591857. https://doi.org/10.1101/2024.04.30.591857

Tags: chicken, epigenomics, grns, machine learning

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Author's response

Nikolai Hecker shared

In the text, you mention that your methods based on sequence level can be applied independently of the alignment with liftOver. Could you elaborate on the similarities and differences with alignment-based comparisons of scATAC-seq datasets?

In both cases you would be utilizing genomic sequences to compare the similarity of cell types between species. Evolutionary pressure acts on transcription factor binding sites (TFBS) to main the function of enhancer regions. So, the conservation of the TFBS will be the most informative features for either approach and will likely be a main contributor to the overall sequence similarity of aligned regions. Our deep learning models learn abstractions, that is the combinations of TFBS, that are most important to distinguish cell types. Instead of comparing the sequence identity of orthologous regions directly, this allows us to compare how similar the most descriptive TFBS for cell types are between species based on what our models learned. This is in particular useful if you compare further distant species for which it is difficult to find orthologous regions. As an illustration, we can lift over at least 10nt for >75% of the differentially accessible regions (DARs), which we used in our study, from the mouse to the human genome. If we try to lift over mouse DARs to the chicken genome it only works for the less than 20% of the DARs.

For DARs of intra-telencephalic neurons, less than 10% of the DARs can be lifted over to the chicken genome likely reflecting their distinct evolutionary trajectories. Hence, for alignment-based methods you would be limited to a small fraction of regions that you can compare between species. Our approaches circumvent those limitations. You can take all the DARs of a cell type and compare the prediction scores and the explanations between models that were trained for different species.

In your pipeline, you don’t include your SCENIC+ to obtain eRegulons of each cell type. Has it been replaced by a TF-MoDISco analysis? What are the differences or benefits?

SCENIC+ is a great tool for obtaining gene regulatory networks for your data set. It provides eRegulons (transcription factors (TF), potential enhancer regions, and links to target genes) and their activity across the cells. Since we can only align a low number of the accessible regions between the chicken and mouse genome, our main focus was not to compare eRegulons between the species. In this study, we put the emphasis on deep learning of cell type specific enhancer codes (combinations of TFBS) and how we can use them to compare cell types across species. A difference compared to SCENIC+ is that we train models specifically to learn sequence features (TFBS) from DARs to distinguish cell types. For SCENIC+, you approach the task from a different angle. You first identify eRegulons and then assess their cell type specificity. Both approaches are quite complementary. From the SCENIC+, you obtain the TFs as part of the eRegulons. In this study, we used TF-MoDISco to detect important sequence patterns and then compared them to known motifs in the cisTarget database, which is part of the SCENIC+ framework, to identify the TFs they correspond to. This process involves a lot of manual curations including to check how the expression of TFs correlates with the importance of detected TFBS across cell types. SCENIC+ detects many of the key TFs for which we identified TFBS with TF-MoDISco in our study and, thus, provides guidance for annotating the TF-MoDISco patterns.

In your preprint, you mainly report conservation examples across species – could you give as an example of divergence and how the four methods are able to visualize such divergence?

That is true. We focused on detecting similarities between cell types in the study and employed four metrics to measure this (three based on deep learning and enhancer codes, one using the transcriptomes). If we compare the similarity between mammalian and avian pallial neurons two groupings stand out by similarity of their enhancer codes and transcriptomes: avian mesopallial and mammalian deep layer neocortical neurons as the first group; and neurons from the avian nido- or hyperpallium and mammalian upper layer or piriform cortical neurons as the second group. These groups of neurons may have diverged from each other during evolution. Whether we can actually talk about divergence in this case is a bit less clear though. For divergence, we would need a point of reference to investigate changes. In this case, we do not know how the neurons of the amniote ancestor looked like and we have too little data to reconstruct the ancestral state. In addition, as Zaremba et al. and Rueda-Alana et al. also show, the similarities between pallial neurons of mammals and chickens do not seem to agree with evolutionary models based on developmental trajectories and brain circuitry formation. This makes it difficult to define pairs of cell types between species a priori to assess divergence.

In the paper, we show orthologous regions that are accessible in corresponding mammal and chicken cell types and their conserved TFBS. Our models can also be used to investigate changes in cell type specificity of orthologous region between species. This could provide hints on how gene regulatory networks have been re-wired during evolution. As an example, we can look at regions that are characteristic for homologous cell types such as oligodendrocytes. This is an example of a region ~37kb upstream of the Kirrel3 gene in the mouse genome, which is only accessible in intermediate (IOL) and mature oligodendrocytes (OL). We can lift over this region to the chicken genome. The orthologous region in the chicken genome is not accessible in oligodendrocytes but in interneurons. Interestingly, KIRREL3 is also not expressed in oligodendrocytes in the chicken telencephalon while the ortholog shows expression in mouse IOL and OL. The prediction scores of our models recapitulate the OL specificity of the mouse region and the change in cell type specificity of the orthologous region in chicken. If we inspect the explanations of our models, we can see that the part, which contains the OL enhancer code, is heavily changed in the chicken region. All of the TFBS (Olig1/2, Sox8, and Sox10 binding sites) that are identified as OL characteristic are either completely absent in the chicken region or contain nucleotide mismatches, insertions, and deletion which most likely renders them non-functional.

The incredible similarity between mesopallium and deep layer neocortex seems not to be related to akin developmental territories nor adult function. Could you speculate onwhy these cell types are so alike?

The mesopallium-deep layer neocortical neuron similarity was quite unexpected for us and, frankly, is a bit puzzling. The transcriptomes of deep layer neurons also show similarities to neurons of the non-avian reptilian dorsal cortex (Tosches et al., 2018 and Zaremba et al., 2024), which could indicate that they emerged in the amniote ancestor and are less diverged than other pallial neurons. Zaremba et al. point out that mammalian deep layer neurons are early born during development and that the avian mesopallium may be homologous to early born neurons in general. So, there might be an evolutionary trajectory, which is distinct from the mammalian upper layer neocortical, piriform cortex, and avian nido-, ento-, and hyperpallial neurons. While mammalian deep layer (L6CT/L6b) neurons are most similar to the chicken mesopallial neurons (EXC_GLU-7 in our study), we also detected some differences. TBR1 is a key TF for mammalian deep layer neurons but our models put less importance on TBR1-binding sites for avian mesopallial neurons. Hence, it is also possible that the similarity is simply a product of convergent evolution within a pool of amniote pallial gene regulatory programs. Investigation of additional amniote species, will help to reconstruct how the different pallial neurons evolved.

Since your deep learning models are based on open genomic regions, have you considered using genome sequence conservation of other species (other reptiles or amphibians)? Could be a hint for cell type conservation?

That is definitely a direction we planned to follow up. Ideally, we would generate single cell multiome data for the majority of amniote species. This is, of course, a long and expensive process. Genomes on the other hand are already available for hundreds of vertebrates. Using our deep learning models and chicken, human, or, mouse-based genome alignments, we can assess for which species the enhancer codes are conserved in orthologous regions. This way we can compare the evolutionary age of genomic regions regarding their specificity for the different cell types. Here, we are a bit limited to species for which we can align a sufficient number of sequences, as outlined above. Nonetheless, this approach may provide interesting insights within the class of Reptilia, for instance.

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