Cephalopods are part of a long tradition in evolutionary neuroscience and are often highlighted as an ideal example of convergent evolution of nervous systems (Young, 1965; Young, 1985; Abbott et al., 1995). Their large brains, relative to body size, and behavioral repertoires offer opportunities for comparative analysis of the evolution of learning and memory mechanisms. Cephalopods possess a sophisticated nervous system that resembles that of vertebrates in relative size and cognitive abilities (Young, 1991; Hochner et al., 2006). These complex behavioral abilities include various forms of learning, problem-solving, and body color and pattern changes used either as predatory or anti-predatory behaviors or for social communication (Fiorito et al., 1990; Langridge et al., 2007; Staudinger et al., 2013; Darmaillacq et al., 2014; Schnell et al., 2015; Richter et al., 2016; Lin and Chiao, 2017; Marini et al., 2017; Mather and Dickel, 2017; Hanlon and Messenger, 2018). The fossil record suggests that cephalopods were more prominent in marine ecosystems over 300 million years ago with a higher biodiversity than today. It is assumed that the rise of bony fishes and resulting competition for resources in that ecological niche played a major role in the extinction of many clades of cephalopods, including all the shelled cephalopods except few members of the subclass Nautiloidea. Today, the class Cephalopoda has over 800 current living species with extremely diversified lifestyles and body shapes (Kröger et al., 2011). The vast majority of current cephalopod species belong to the soft-bodied subclass Coleoidea (cuttlefishes, octopuses, and squids), and a small handful belong to the subclass Nautiloidea (nautiluses). Coleoid cephalopods, which have internalized or lost their shell over evolutionary time, were able to swim more easily and could compete with other predators in open waters. The continuous competition with other animals to access resources has been proposed to have been the main trigger for the selection of cephalopods that could develop specialized strategies for accessing resources unavailable to competing species. In coleoid cephalopods, this evolutionary process resulted in larger brains, camera-type eyes, as well as abilities to voluntarily change their skin pattern and texture for camouflage and social communication (Young, 1973; Hanlon and Messenger, 2018; Amodio et al., 2019).

The European squid Loligo vulgaris is well known for its economic role in the food and fishing industry but is much less studied for its fascinating biology. This species is known for its striking ability for camouflage typically in open-water environments by the use of its specialized iridescent cells to reflect light in different manners depending on its surroundings. The neuroanatomy and nervous system morphology of L. vulgaris have been described in detail in histological studies (Young, 1976; Wild et al., 2015). Detailed staging of the embryonic development of the related species Loligo pealii was established and can be used as a reference for L. vulgaris (Arnold, 1965). While the gross anatomy of squids is well known from histology and morphological studies, we are currently lacking resolution at the molecular level to identify genes that specify the many cell types that make up the unique cephalopod body-plan and nervous system. Since the publication of the first complete genome sequence of Octopus bimaculoides, there has been increasing interest in cephalopod biology and neuroscience. In addition, the availability of genomic sequences of other cephalopods, such as Euprymna scolopes (Sepiida), Architeuthis dux (Oegopsida), and Nautilus pompilius (Nautilida), opens the possibility for molecular comparisons between these distant clades of cephalopods with drastically different morphologies and lifestyles. These comparisons constitute a major step for understanding the molecular basis of cephalopod evolution (Albertin et al., 2015; Belcaid et al., 2019; da Fonseca et al., 2020; Zhang et al., 2021).

Single-cell transcriptomics experiments in different cephalopods have shed light on the cell type diversity of the O. bimaculoides visual system, the brain of Octopus vulgaris, and the nervous system of the squid Euprymna berryi (Gavriouchkina et al., 2022; Songco-Casey et al., 2022; Styfhals et al., 2022).

To gain further insights into the cellular diversity and cell type-specific gene expression in squids, we performed single-cell transcriptomics on whole L. vulgaris heads (stages 28–30 based on Arnold, 1965, pre-hatchling stage). Additionally, we used hybridization chain reaction in situ hybridization (HCR ISH) to visualize gene expression in situ and correlate single-cell transcriptomes with anatomical features. By combining these methods, we were able to identify and assess common cell types found in most bilaterians, such as different types of neurons, muscles, and connective tissues. Interestingly, even in these canonical tissues, we found certain clusters that highly express cephalopod-specific genes, as exemplified by some subtypes of connective tissues. Thus, while cells can be assigned a certain identity based on well-characterized marker genes, such as collagens, these cell types have likely further diverged in cephalopods. Neural tissues are prominent in the head and include the brain, optic ganglia, eyes, and other sensory organs. We identified and analyzed new markers of different cells in the nervous system, including widely expressed pan-neuronal genes, genes marking distinct stages of neural differentiation, markers for neural subsets, as well as sensory cell types. In addition, we also characterized cephalopod-specific cell types, such as the chromatophores or cells of the suckers. Our study provides new insights into the transcriptomic signatures of cell types in L. vulgaris. By analyzing the expression patterns of many cell type-specific genes, we assessed the conservation of features previously described in other animal clades but also previously unknown features and modalities of cell types that appear specific for cephalopods.

We here provide molecular insights into the cell types present in the head of L. vulgaris by combining two state-of-the-art techniques HCR ISH and scRNAseq. The combination of these two techniques allowed us to provide insights into the different cell types that make up the L. vulgaris head, into the process of nervous system development and the identity of neuronal subtypes. The data produced for this article enabled the identification of 34 cell clusters that we used as a correlate for cell type diversity. Although the data can provide a broad overview of cell diversity, we note that few cell types that would be expected in the head of cephalopods such as components of the vascular and digestive systems could not be confidently identified in this study. The apparent lack of resolution in the clusters and their low number is likely to be in large part caused by the lack of completeness of the reference transcriptome, which resulted in a lower mapping rate of the single-cell reads. It has been shown in other cephalopods that having a high-quality genome largely contributes to improving cluster resolution (Gavriouchkina et al., 2022; Songco-Casey et al., 2022; Styfhals et al., 2022). Because of the lack of a reference genome for L. vulgaris, a non-redundant transcriptome was used as a mapping reference in this study, which has shown to be sufficient for identifying cell types and describing the expression some of their characteristic marker genes but is however not sufficient to affirm full coverage of cell type diversity of this organism. The use of a transcriptome instead of a genome as a reference has been previously shown to enable molecular description of cell types but can lack in resolution (Duruz et al., 2021). Additionally, it is likely that the transcriptome assembly could be improved by obtaining more RNA from L. vulgaris by including different embryonic stages and adult stages. We are convinced that re-running this analysis with a better reference would reinforce our analysis and provide better cell type resolution. In our analysis of the nervous system, we were able to recognize broad neuronal markers that are expressed throughout the nervous system while showing different levels of expression in predicted neuronal cell types. These newly identified neuronal markers will provide a new perspective to study the characteristics of the nervous systems of cephalopods. Particularly, the genes tetraspanin-8 and amyloid beta-binding-like show strikingly elevated expression pan-neuronally. Besides the identified sensory cells, the homogeneity of the neuronal clusters was rather surprising. Different neuronal subpopulations were expected to be identified by the expression of genes neurotransmitter synthesis pathway to distinguish neurons by their chemical modalities. Such fine distinctions between neuronal populations could be found in the nervous systems of E. berryi and O. vulgaris (Gavriouchkina et al., 2022; Songco-Casey et al., 2022; Styfhals et al., 2022). It is likely that this lack of resolution is due to missing genes in our reference transcriptome, which would classically contribute to distinguishing neuronal populations. The analysis of the expression of two highly expressed members of the ELAV family elav-like 1 and elav-like 2 revealed that although they can be found in most lobes of the nervous system, their expression levels differ in different clusters. Previous studies of ELAV proteins in S. officinalis revealed that the gene Sof-Elav1 was expressed in all the lobes of the nervous system but also during early embryogenesis while the other gene Sof-Elav2 was not specific to neurons (Buresi et al., 2013). In O. bimaculoides, a single neuronal ELAV protein was identified and was observed to be also expressed during neurogenesis and could be used as a neuronal marker to study nervous system development during embryogenesis, similarly to Sepia (Shigeno et al., 2015). It is interesting to note that contrarily to what was described in cuttlefish and octopus we show two cell type-specific elav genes, which are both expressed in neurons. This challenges the hypothesis of a duplication of the elav gene during lophotrochozoan evolution proposed after the observation of two elav genes in Capitella sp., one of which is expressed in neurons while the other is expressed mainly in the mesoderm (Meyer and Seaver, 2009). The identification of two neuronal elav genes in L. vulgaris raises the question of whether they are orthologous to the genes studies in Sepia and Octopus but their function is different. Alternatively, these two neuronal elav genes could the result of another duplication of the neuronal elav. Such questions would need to be further investigated by more thoroughly studying the sequence homology of these genes and reconstruct their phylogeny. We identified key transcription factors involved in stem cells and development. The expression of these genes likely reflects different states of neuronal differentiation and suggests a migration of neuronal progenitors towards the nervous system. We could show that proliferating cells that express of the transcription factor e2f3 are located in the lateral lips near the nervous system. We interestingly observed the expression of asc in the same region, together with several proliferation markers. Extensive study of neurogenesis in D. melanogaster has shown that the proneural complex asc can determine the neuronal fate of quiescent multipotent ectodermal cells (Campuzano and Modolell, 1992; Skeath and Doe, 1996). In vertebrates, on the other hand, asc is expressed in cells that have already undergone neuronal specification but are self-renewing (Bertrand et al., 2002; Soares et al., 2022). Studies of neurogenesis in the annelid Capitella teleta have shown that, similarly to vetrebrates, proneural genes such as asc are expressed in self-renewing neuronal progenitors (Sur et al., 2020). Our data suggests that asc is also expressed in proliferating cells, similiarly to vertrebrates and C. teleta but unlike Drosophila. This is consistent with documented co-expression of asc and the mitotic marker phosphor-histone H3 in O. vulgaris (Deryckere et al., 2021). The observation of soxB1 expression together with asc strengthened their characterization as neuronal progenitors due to the documented role of this gene in neuronal differentiation (Miyagi et al., 2009). However, strong soxB1 expression in differentiated neurons has been shown in the gastropod Patella vulgate and in the cephalopods S. officinalis and O. vulgaris (Le Gouar et al., 2004Focareta and Cole, 2016; Deryckere et al., 2021). In L. vulgaris, we observe similar results where soxB1 is expressed in the lateral lips together with asc but is also expressed in differentiated neurons. This was consistently shown by the HCR ISH experiments as well as the single-cell analysis. Therefore, the hypothesis proposed for the action of soxB1 in the early stages as well as the later stages of neuronal differentiation in O. vulgaris is consistent with our data (Deryckere et al., 2021). The unexpected expression of foxD1 in the nervous system of L. vulgaris, together with existing evidence of the role of this gene in neuronal development, motivated the grouping of the foxD1⁺ clusters with other clusters characterized as neuronal progenitors for further analysis (Herrera et al., 2004; Polevoy et al., 2017; Newman et al., 2018; Janssen et al., 2022). Our trajectory inference analysis using monocle3 and slingshot showed that foxD1 is expressed in cells that could represent an intermediate neuronal differentiation stage following the expression of asc but before the expression of amyloid beta-binding-like and the second peak of soxB1 expression. Such a role has been proposed in O. vulgaris in the cells expressing neuroD that are expressed in a gradient between the lateral lip and the central nervous system, which may indicate a pattern of migration of differentiating neurons (Deryckere et al., 2021). We could, however, not correlate these results with our hypothesis of an intermediate foxD1-expressing stage because neuroD expression could not be detected in specific clusters in our single-cell dataset. Although the trajectories we calculated point towards a role of FoxD1 in neuronal differentiation, the relatively poor cluster resolution and the lack of cells along the trajectory axes shown in Figure 3 suggest that this hypothesis would require further investigation that should include a more complete set of genes that have been involved with cephalopod neurogenesis. In our analysis of sensory cells, we provide valuable information regarding the different sensory systems of L. vulgaris and could identify photoreceptors and additionally describe newly identified markers of the olfactory organ and mechanosensory cells. The sensory cells of cephalopods have been extensively studied and are known to be very complex and can have multiple sensory modalities (van Giesen et al., 2020; Buresch et al., 2022). In O. bimaculoides and S. officinalis, several chemosensory receptors could be identified and characterized (van Giesen et al., 2020; Andouche et al., 2021). In our analysis of the L. vulgaris head, we were however not able to identify putative chemosensory genes, likely due to the absence of characteristic receptors in our transcriptome. The chemosensory function of cells analyzed here was only inferred because of their expression in the olfactory organ. A full analysis of chemosensory genes in L. vulgaris would be required to be able to truly describe the diversity of their sensory cell types. Finally, we further characterize the highly specialized epidermis of L. vulgaris and cephalopod-specific cell types such as suckers or iridophores. The expression of a few genes facilitated the characterization of some of these cell types as exemplified by the expression of egf-like in epidermal cells, suckerin-6 in suckers, and reflectin-8 in iridophores. However, many cluster-specific genes could not be assigned to a specific function. The abundance of papilin-expressing cells on the surface of the epidermis is striking and had not been previously documented. The gene madnf is known to play a role in the maturation and maintenance of dopaminergic neurons in D. melanogaster and C. elegans (Palgi et al., 2009; Richman et al., 2018). We found discrepancies between the observation of madnf in non-neuronal clusters and in a small cluster of mechanosensory cells and the expression patterns shown by HCR ISH, where expression is found in different areas of the brain, optic lobes, and olfactory organs. Similarly, the expression of reflectin-8 is not limited to the chromatophores but is also in the nervous system. The expression of these non-neuronal makers inside the nervous system results highlights the importance of considering non-neuronal cell types, even when aiming to answer neurobiological questions. Many cell types remain unidentified, and further characterization of these cells and the function of the genes that they differentially express could advance our understanding of cephalopod biology. The combination of high-resolution imaging rendered possible by HCR in conjunction with the data provided by single-cell transcriptomics enabled us to extend our understanding of the cell types of cephalopods. The experimental approach is efficient to identify and explore conserved sets of genes conferring cell type identity in these animals while comparing cell type markers across different clades. On the other hand, the accurate anatomical description gained with HCR ISH allowed us to find previously undescribed cell type-specific genes by identifying the anatomical features in which they are expressed, enabling the exploration not only of conserved cell types but also novel ones. By applying single-cell RNA sequencing in a member of this fascinating clade of mollusks and by correlating this data with high-resolution mRNA imaging techniques, we can further understand the main molecular features that drive cell type identity in L. vulgaris. The analysis of the expression of selected genes of interest can provide insight into their function, which can be a starting point for future investigations of cephalopod cell types. Further studies of the nervous system of squids and the different cell types that compose them could help fill important gaps in our understanding of the evolution of the nervous system and could provide insights into how brains complexified independently in very distant clades of animals. By comparing the molecular features of neuronal cell types of cephalopods with other species, we could better understand the evolutionary history of the unique brains of cephalopods and identify what confers them such unique capabilities. This is a significant step in the study of convergent evolution of brains and provides a valuable resource for future research on cephalopods.

Sequencing data have been deposited in GEO under accession code GSE200108.

1. 1. Duruz J
   2. Sprecher M
   3. Kaldun JC
   4. Al-Soudy A
   5. Tschanz-Lischer H
   6. Nicholson P
   7. Bruggmann R
   8. Sprecher SG

   (2022) NCBI Gene Expression Omnibus

   ID GSE200108. Molecular characterization of cell types in the squid Loligo vulgaris.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE200108

1. Book

   1. Abbott NJ
   2. Williamson R
   3. Maddock L

   (1995) Cephalopod Neurobiology Neuroscience Studies in Squid, Octopus and Cuttlefish

   Oxford University Press.

   https://doi.org/10.1093/acprof:oso/9780198547907.001.0001

   • Google Scholar
2. 1. Albertin CB
   2. Simakov O
   3. Mitros T
   4. Wang ZY
   5. Pungor JR
   6. Edsinger-Gonzales E
   7. Brenner S
   8. Ragsdale CW
   9. Rokhsar DS

   (2015) The octopus genome and the evolution of cephalopod neural and morphological novelties

   Nature 524:220–224.

   https://doi.org/10.1038/nature14668

   • PubMed
   • Google Scholar
3. 1. Alqadah A
   2. Hsieh YW
   3. Vidal B
   4. Chang C
   5. Hobert O
   6. Chuang CF

   (2015) Postmitotic diversification of olfactory neuron types is mediated by differential activities of the HMG-box transcription factor Sox-2

   The EMBO Journal 34:2574–2589.

   https://doi.org/10.15252/embj.201592188

   • PubMed
   • Google Scholar
4. 1. Amodio P
   2. Boeckle M
   3. Schnell AK
   4. Ostojíc L
   5. Fiorito G
   6. Clayton NS

   (2019) Grow smart and die young: why did cephalopods evolve intelligence?

   Trends in Ecology & Evolution 34:45–56.

   https://doi.org/10.1016/j.tree.2018.10.010

   • PubMed
   • Google Scholar
5. 1. Andouche A
   2. Valera S
   3. Baratte S

   (2021) Exploration of chemosensory ionotropic receptors in cephalopods: the IR25 gene is expressed in the olfactory organs, suckers, and fins of Sepia officinalis

   Chemical Senses 46:bjab047.

   https://doi.org/10.1093/chemse/bjab047

   • PubMed
   • Google Scholar
6. Book

   1. Apte SS

   (2020) ADAMTS proteins: concepts, challenges, and prospects

   In: Apte SS, editors. ADAMTS Proteases: Methods and Protocols. New York, NY: Springer. pp. 1–12.

   https://doi.org/10.1007/978-1-4939-9698-8_1

   • Google Scholar
7. 1. Arefin B
   2. Parvin F
   3. Bahrampour S
   4. Stadler CB
   5. Thor S

   (2019) Drosophila neuroblast selection is gated by Notch, snail, SoxB, and EMT gene interplay

   Cell Reports 29:3636–3651.

   https://doi.org/10.1016/j.celrep.2019.11.038

   • PubMed
   • Google Scholar
8. 1. Arendt D

   (2003) Evolution of eyes and photoreceptor cell types

   The International Journal of Developmental Biology 47:563–571.

   https://doi.org/10.1387/ijdb.14756332

   • PubMed
   • Google Scholar
9. 1. Arnold JM

   (1965) Normal embryonic stages of the squid, Loligo pealii (lesueur)

   The Biological Bulletin 128:24–32.

   https://doi.org/10.2307/1539386

   • Google Scholar
10. 1. Arsic N
    2. Rajic T
    3. Stanojcic S
    4. Goodfellow PN
    5. Stevanovic M

    (1998) Characterisation and mapping of the human SOX14 gene

    Cytogenetics and Cell Genetics 83:139–146.

    https://doi.org/10.1159/000015149

    • PubMed
    • Google Scholar
11. 1. Attwooll C
    2. Lazzerini Denchi E
    3. Helin K

    (2004) The E2F family: specific functions and overlapping interests

    The EMBO Journal 23:4709–4716.

    https://doi.org/10.1038/sj.emboj.7600481

    • PubMed
    • Google Scholar
12. 1. Belcaid M
    2. Casaburi G
    3. McAnulty SJ
    4. Schmidbaur H
    5. Suria AM
    6. Moriano-Gutierrez S
    7. Pankey MS
    8. Oakley TH
    9. Kremer N
    10. Koch EJ
    11. Collins AJ
    12. Nguyen H
    13. Lek S
    14. Goncharenko-Foster I
    15. Minx P
    16. Sodergren E
    17. Weinstock G
    18. Rokhsar DS
    19. McFall-Ngai M
    20. Simakov O
    21. Foster JS
    22. Nyholm SV

    (2019) Symbiotic organs shaped by distinct modes of genome evolution in cephalopods

    PNAS 116:3030–3035.

    https://doi.org/10.1073/pnas.1817322116

    • PubMed
    • Google Scholar
13. 1. Bertrand N
    2. Castro DS
    3. Guillemot F

    (2002) Proneural genes and the specification of neural cell types

    Nature Reviews. Neuroscience 3:517–530.

    https://doi.org/10.1038/nrn874

    • PubMed
    • Google Scholar
14. 1. Budelmann BU
    2. Bleckmann H

    (1988) A lateral line analogue in cephalopods: water waves generate microphonic potentials in the epidermal head lines of sepia and lolliguncula

    Journal of Comparative Physiology. A, Sensory, Neural, and Behavioral Physiology 164:1–5.

    https://doi.org/10.1007/BF00612711

    • PubMed
    • Google Scholar
15. 1. Buresch KC
    2. Sklar K
    3. Chen JY
    4. Madden SR
    5. Mongil AS
    6. Wise GV
    7. Boal JG
    8. Hanlon RT

    (2022) Contact chemoreception in multi-modal sensing of prey by octopus

    Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology 208:435–442.

    https://doi.org/10.1007/s00359-022-01549-y

    • PubMed
    • Google Scholar
16. 1. Buresi A
    2. Canali E
    3. Bonnaud L
    4. Baratte S

    (2013) Delayed and asynchronous ganglionic maturation during cephalopod neurogenesis as evidenced by sof-elav1 expression in embryos of Sepia officinalis (Mollusca, cephalopoda)

    The Journal of Comparative Neurology 521:1482–1496.

    https://doi.org/10.1002/cne.23231

    • PubMed
    • Google Scholar
17. 1. Cabrera CV
    2. Martinez-Arias A
    3. Bate M

    (1987) The expression of three members of the achaete-scute gene complex correlates with neuroblast segregation in Drosophila

    Cell 50:425–433.

    https://doi.org/10.1016/0092-8674(87)90496-x

    • PubMed
    • Google Scholar
18. 1. Campuzano S
    2. Modolell J

    (1992) Patterning of the Drosophila nervous system: the achaete-scute gene complex

    Trends in Genetics 8:202–208.

    https://doi.org/10.1016/0168-9525(92)90234-u

    • PubMed
    • Google Scholar
19. 1. Choi HMT
    2. Calvert CR
    3. Husain N
    4. Huss D
    5. Barsi JC
    6. Deverman BE
    7. Hunter RC
    8. Kato M
    9. Lee SM
    10. Abelin ACT
    11. Rosenthal AZ
    12. Akbari OS
    13. Li Y
    14. Hay BA
    15. Sternberg PW
    16. Patterson PH
    17. Davidson EH
    18. Mazmanian SK
    19. Prober DA
    20. van de Rijn M
    21. Leadbetter JR
    22. Newman DK
    23. Readhead C
    24. Bronner ME
    25. Wold B
    26. Lansford R
    27. Sauka-Spengler T
    28. Fraser SE
    29. Pierce NA

    (2016) Mapping a multiplexed zoo of mrna expression

    Development 143:3632–3637.

    https://doi.org/10.1242/dev.140137

    • PubMed
    • Google Scholar
20. 1. Crawford K
    2. Diaz Quiroz JF
    3. Koenig KM
    4. Ahuja N
    5. Albertin CB
    6. Rosenthal JJC

    (2020) Highly efficient knockout of a squid pigmentation gene

    Current Biology 30:3484–3490.

    https://doi.org/10.1016/j.cub.2020.06.099

    • PubMed
    • Google Scholar
21. 1. Crookes WJ
    2. Ding L-L
    3. Huang QL
    4. Kimbell JR
    5. Horwitz J
    6. McFall-Ngai MJ

    (2004) Reflectins: the unusual proteins of squid reflective tissues

    Science 303:235–238.

    https://doi.org/10.1126/science.1091288

    • PubMed
    • Google Scholar
22. 1. da Fonseca RR
    2. Couto A
    3. Machado AM
    4. Brejova B
    5. Albertin CB
    6. Silva F
    7. Gardner P
    8. Baril T
    9. Hayward A
    10. Campos A
    11. Ribeiro ÂM
    12. Barrio-Hernandez I
    13. Hoving H-J
    14. Tafur-Jimenez R
    15. Chu C
    16. Frazão B
    17. Petersen B
    18. Peñaloza F
    19. Musacchia F
    20. Alexander GC
    21. Osório H
    22. Winkelmann I
    23. Simakov O
    24. Rasmussen S
    25. Rahman MZ
    26. Pisani D
    27. Vinther J
    28. Jarvis E
    29. Zhang G
    30. Strugnell JM
    31. Castro LFC
    32. Fedrigo O
    33. Patricio M
    34. Li Q
    35. Rocha S
    36. Antunes A
    37. Wu Y
    38. Ma B
    39. Sanges R
    40. Vinar T
    41. Blagoev B
    42. Sicheritz-Ponten T
    43. Nielsen R
    44. Gilbert MTP

    (2020) A draft genome sequence of the elusive giant squid, architeuthis Dux

    GigaScience 9:giz152.

    https://doi.org/10.1093/gigascience/giz152

    • PubMed
    • Google Scholar
23. Book

    1. Darmaillacq AS
    2. Dickel L
    3. Mather J

    (2014) Cephalopod Cognition

    Cambridge: Cambridge University Press.

    https://doi.org/10.1017/CBO9781139058964

    • Google Scholar
24. 1. Davies A
    2. Schertler GFX
    3. Gowen BE
    4. Saibil HR

    (1996) Projection structure of an invertebrate rhodopsin

    Journal of Structural Biology 117:36–44.

    https://doi.org/10.1006/jsbi.1996.0067

    • PubMed
    • Google Scholar
25. 1. DeGregori J
    2. Johnson DG

    (2006) Distinct and overlapping roles for E2F family members in transcription, proliferation and apoptosis

    Current Molecular Medicine 6:739–748.

    https://doi.org/10.2174/1566524010606070739

    • PubMed
    • Google Scholar
26. 1. Demski LS

    (1992) Chromatophore systems in teleosts and cephalopods: a levels oriented analysis of convergent systems

    Brain, Behavior and Evolution 40:141–156.

    https://doi.org/10.1159/000113909

    • PubMed
    • Google Scholar
27. 1. Derby CD

    (2014) Cephalopod ink: production, chemistry, functions and applications

    Marine Drugs 12:2700–2730.

    https://doi.org/10.3390/md12052700

    • PubMed
    • Google Scholar
28. 1. Deryckere A
    2. Styfhals R
    3. Elagoz AM
    4. Maes GE
    5. Seuntjens E

    (2021) Identification of neural progenitor cells and their progeny reveals long distance migration in the developing octopus brain

    eLife 10:e69161.

    https://doi.org/10.7554/eLife.69161

    • PubMed
    • Google Scholar
29. 1. Dubas F
    2. Hanlon RT
    3. Ferguson GP
    4. Pinsker HM

    (1986) Localization and stimulation of chromatophore motoneurones in the brain of the squid, lolliguncula brevis

    The Journal of Experimental Biology 121:1–25.

    https://doi.org/10.1242/jeb.121.1.1

    • PubMed
    • Google Scholar
30. 1. Duruz J
    2. Kaltenrieder C
    3. Ladurner P
    4. Bruggmann R
    5. Martìnez P
    6. Sprecher SG

    (2021) Acoel single-cell transcriptomics: cell type analysis of a deep branching bilaterian

    Molecular Biology and Evolution 38:1888–1904.

    https://doi.org/10.1093/molbev/msaa333

    • PubMed
    • Google Scholar
31. 1. Dynlacht BD
    2. Brook A
    3. Dembski M
    4. Yenush L
    5. Dyson N

    (1994) DNA-binding and trans-activation properties of Drosophila E2F and DP proteins

    PNAS 91:6359–6363.

    https://doi.org/10.1073/pnas.91.14.6359

    • PubMed
    • Google Scholar
32. 1. Elliott DA
    2. Solloway MJ
    3. Wise N
    4. Biben C
    5. Costa MW
    6. Furtado MB
    7. Lange M
    8. Dunwoodie S
    9. Harvey RP

    (2006) A tyrosine-rich domain within homeodomain transcription factor nkx2-5 is an essential element in the early cardiac transcriptional regulatory machinery

    Development 133:1311–1322.

    https://doi.org/10.1242/dev.02305

    • PubMed
    • Google Scholar
33. 1. Falanga A
    2. Lombardi L
    3. Franci G
    4. Vitiello M
    5. Iovene MR
    6. Morelli G
    7. Galdiero M
    8. Galdiero S

    (2016) Marine antimicrobial peptides: nature provides templates for the design of novel compounds against pathogenic bacteria

    International Journal of Molecular Sciences 17:785.

    https://doi.org/10.3390/ijms17050785

    • PubMed
    • Google Scholar
34. 1. Fessler JH
    2. Kramerova I
    3. Kramerov A
    4. Chen Y
    5. Fessler LI

    (2004) Papilin, a novel component of basement membranes, in relation to ADAMTS metalloproteases and ECM development

    The International Journal of Biochemistry & Cell Biology 36:1079–1084.

    https://doi.org/10.1016/j.biocel.2003.12.010

    • PubMed
    • Google Scholar
35. 1. Fiorito G
    2. von Planta C
    3. Scotto P

    (1990) Problem solving ability of octopus vulgaris lamarck (mollusca, cephalopoda)

    Behavioral and Neural Biology 53:217–230.

    https://doi.org/10.1016/0163-1047(90)90441-8

    • PubMed
    • Google Scholar
36. 1. Focareta L
    2. Cole AG

    (2016) Analyses of sox-B and sox-E family genes in the cephalopod sepia officinalis: revealing the conserved and the unusual

    PLOS ONE 11:e0157821.

    https://doi.org/10.1371/journal.pone.0157821

    • PubMed
    • Google Scholar
37. 1. Fu L
    2. Niu B
    3. Zhu Z
    4. Wu S
    5. Li W

    (2012) CD-HIT: accelerated for clustering the next-generation sequencing data

    Bioinformatics 28:3150–3152.

    https://doi.org/10.1093/bioinformatics/bts565

    • PubMed
    • Google Scholar
38. Preprint

    1. Gavriouchkina D
    2. Tan Y
    3. Ziadi-Künzli F
    4. Hasegawa Y
    5. Piovani L
    6. Zhang L
    7. Sugimoto C
    8. Luscombe N
    9. Marlétaz F
    10. Rokhsar DS

    (2022) A Single-Cell Atlas of Bobtail Squid Visual and Nervous System Highlights Molecular Principles of Convergent Evolution

    bioRxiv.

    https://doi.org/10.1101/2022.05.26.490366

    • Google Scholar
39. 1. Gogineni V
    2. Hamann MT

    (2018) Marine natural product peptides with therapeutic potential: chemistry, biosynthesis, and pharmacology

    Biochimica et Biophysica Acta. General Subjects 1862:81–196.

    https://doi.org/10.1016/j.bbagen.2017.08.014

    • PubMed
    • Google Scholar
40. 1. Gonzalez-Bellido PT
    2. Wardill TJ
    3. Buresch KC
    4. Ulmer KM
    5. Hanlon RT

    (2014) Expression of squid iridescence depends on environmental luminance and peripheral ganglion control

    The Journal of Experimental Biology 217:850–858.

    https://doi.org/10.1242/jeb.091884

    • PubMed
    • Google Scholar
41. Book

    1. Hanlon RT
    2. Messenger JB

    (2018) Cephalopod Behaviour

    Cambridge: Cambridge University Press.

    https://doi.org/10.1017/9780511843600

    • Google Scholar
42. 1. Herrera E
    2. Marcus R
    3. Li S
    4. Williams SE
    5. Erskine L
    6. Lai E
    7. Mason C

    (2004) Foxd1 is required for proper formation of the optic chiasm

    Development 131:5727–5739.

    https://doi.org/10.1242/dev.01431

    • PubMed
    • Google Scholar
43. 1. Hochner B
    2. Shomrat T
    3. Fiorito G

    (2006) The octopus: a model for a comparative analysis of the evolution of learning and memory mechanisms

    The Biological Bulletin 210:308–317.

    https://doi.org/10.2307/4134567

    • PubMed
    • Google Scholar
44. 1. Huo Z
    2. Tang S
    3. Park Y
    4. Tseng G

    (2020) P-value evaluation, variability index and biomarker categorization for adaptively weighted fisher’s meta-analysis method in omics applications

    Bioinformatics 36:524–532.

    https://doi.org/10.1093/bioinformatics/btz589

    • PubMed
    • Google Scholar
45. Software

    1. Jackcava

    (2022) SeuratToMonocle3, version 0.1.0

    Github.

    https://github.com/Jackcava/Seurat_to_Monocle
46. 1. Janssen R
    2. Schomburg C
    3. Prpic NM
    4. Budd GE

    (2022) A comprehensive study of arthropod and onychophoran Fox gene expression patterns

    PLOS ONE 17:e0270790.

    https://doi.org/10.1371/journal.pone.0270790

    • PubMed
    • Google Scholar
47. 1. Kier WM
    2. Stella MP

    (2007) The arrangement and function of octopus arm musculature and connective tissue

    Journal of Morphology 268:831–843.

    https://doi.org/10.1002/jmor.10548

    • PubMed
    • Google Scholar
48. 1. Komak S
    2. Boal JG
    3. Dickel L
    4. Budelmann BU

    (2005) Behavioural responses of juvenile cuttlefish (Sepia officinalis) to local water movements

    Marine and Freshwater Behaviour and Physiology 38:117–125.

    https://doi.org/10.1080/10236240500139206

    • Google Scholar
49. 1. Kramerova IA
    2. Kawaguchi N
    3. Fessler LI
    4. Nelson RE
    5. Chen Y
    6. Kramerov AA
    7. Kusche-Gullberg M
    8. Kramer JM
    9. Ackley BD
    10. Sieron AL
    11. Prockop DJ
    12. Fessler JH

    (2000) Papilin in development; a pericellular protein with a homology to the ADAMTS metalloproteinases

    Development 127:5475–5485.

    https://doi.org/10.1242/dev.127.24.5475

    • PubMed
    • Google Scholar
50. 1. Kramerova IA
    2. Kramerov AA
    3. Fessler JH

    (2003) Alternative splicing of papilin and the diversity of Drosophila extracellular matrix during embryonic morphogenesis

    Developmental Dynamics 226:634–642.

    https://doi.org/10.1002/dvdy.10265

    • PubMed
    • Google Scholar
51. 1. Kröger B
    2. Vinther J
    3. Fuchs D

    (2011) Cephalopod origin and evolution: a congruent picture emerging from fossils, development and molecules

    BioEssays 33:602–613.

    https://doi.org/10.1002/bies.201100001

    • PubMed
    • Google Scholar
52. 1. Kumar A
    2. Kannan S
    3. Lescar J
    4. Verma C
    5. Miserez A

    (2016) Squid’s suckerin proteins in bits & bytes

    Biophysical Journal 110:341a.

    https://doi.org/10.1016/j.bpj.2015.11.1835

    • Google Scholar
53. 1. Langridge KV
    2. Broom M
    3. Osorio D

    (2007) Selective signalling by cuttlefish to predators

    Current Biology 17:R1044–R1045.

    https://doi.org/10.1016/j.cub.2007.10.028

    • PubMed
    • Google Scholar
54. 1. Le Gouar M
    2. Guillou A
    3. Vervoort M

    (2004) Expression of a SoxB and a wnt2/13 gene during the development of the mollusc Patella vulgata

    Development Genes and Evolution 214:250–256.

    https://doi.org/10.1007/s00427-004-0399-z

    • PubMed
    • Google Scholar
55. 1. Lin IR
    2. Chiao CC

    (2017) Visual equivalence and amodal completion in cuttlefish

    Frontiers in Physiology 8:40.

    https://doi.org/10.3389/fphys.2017.00040

    • PubMed
    • Google Scholar
56. 1. Loi PK
    2. Tublitz NJ

    (2000) Roles of glutamate and FMRFamide-related peptides at the chromatophore neuromuscular junction in the cuttlefish, Sepia officinalis

    The Journal of Comparative Neurology 420:499–511.

    https://doi.org/10.1002/(sici)1096-9861(20000515)420:4<499::aid-cne7>3.0.co;2-e

    • PubMed
    • Google Scholar
57. 1. Lun ATL
    2. Bach K
    3. Marioni JC

    (2016) Pooling across cells to normalize single-cell RNA sequencing data with many zero counts

    Genome Biology 17:75.

    https://doi.org/10.1186/s13059-016-0947-7

    • PubMed
    • Google Scholar
58. Book

    1. Marini G
    2. De Sio F
    3. Ponte G
    4. Fiorito G

    (2017) 1.24 - behavioral analysis of learning and memory in cephalopods

    In: Byrne JH, editors. Learning and Memory: A Comprehensive Reference (Second Edition). Oxford: Academic Press. pp. 441–462.

    https://doi.org/10.1016/B978-0-12-809324-5.21024-9

    • Google Scholar
59. 1. Maselli V
    2. Galdiero E
    3. Salzano AM
    4. Scaloni A
    5. Maione A
    6. Falanga A
    7. Naviglio D
    8. Guida M
    9. Di Cosmo A
    10. Galdiero S

    (2020) OctoPartenopin: identification and preliminary characterization of a novel antimicrobial peptide from the suckers of Octopus vulgaris

    Marine Drugs 18:380.

    https://doi.org/10.3390/md18080380

    • PubMed
    • Google Scholar
60. 1. Mather JA
    2. Dickel L

    (2017) Cephalopod complex cognition

    Current Opinion in Behavioral Sciences 16:131–137.

    https://doi.org/10.1016/j.cobeha.2017.06.008

    • Google Scholar
61. 1. McCarthy DJ
    2. Campbell KR
    3. Lun ATL
    4. Wills QF

    (2017) Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

    Bioinformatics 33:1179–1186.

    https://doi.org/10.1093/bioinformatics/btw777

    • PubMed
    • Google Scholar
62. 1. Meyer NP
    2. Seaver EC

    (2009) Neurogenesis in an annelid: characterization of brain neural precursors in the polychaete capitella sp-I

    Developmental Biology 335:237–252.

    https://doi.org/10.1016/j.ydbio.2009.06.017

    • PubMed
    • Google Scholar
63. 1. Miyagi S
    2. Kato H
    3. Okuda A

    (2009) Role of SoxB1 transcription factors in development

    Cellular and Molecular Life Sciences 66:3675–3684.

    https://doi.org/10.1007/s00018-009-0097-0

    • PubMed
    • Google Scholar
64. 1. Mobley AS
    2. Michel WC
    3. Lucero MT

    (2008) Odorant responsiveness of squid olfactory receptor neurons

    Anatomical Record 291:763–774.

    https://doi.org/10.1002/ar.20704

    • PubMed
    • Google Scholar
65. 1. Monk PD
    2. Carne A
    3. Liu SH
    4. Ford JW
    5. Keen JN
    6. Findlay JBC

    (1996) Isolation, cloning, and characterisation of a Trp homologue from squid (Loligo forbesi) photoreceptor membranes

    Journal of Neurochemistry 67:2227–2235.

    https://doi.org/10.1046/j.1471-4159.1996.67062227.x

    • PubMed
    • Google Scholar
66. 1. Napoli FR
    2. Daly CM
    3. Neal S
    4. McCulloch KJ
    5. Zaloga AR
    6. Liu A
    7. Koenig KM

    (2022) Cephalopod retinal development shows vertebrate-like mechanisms of neurogenesis

    Current Biology 32:5045–5056.

    https://doi.org/10.1016/j.cub.2022.10.027

    • PubMed
    • Google Scholar
67. 1. Newman EA
    2. Kim DW
    3. Wan J
    4. Wang J
    5. Qian J
    6. Blackshaw S

    (2018) Foxd1 is required for terminal differentiation of anterior hypothalamic neuronal subtypes

    Developmental Biology 439:102–111.

    https://doi.org/10.1016/j.ydbio.2018.04.012

    • PubMed
    • Google Scholar
68. 1. Palgi M
    2. Lindström R
    3. Peränen J
    4. Piepponen TP
    5. Saarma M
    6. Heino TI

    (2009) Evidence that dmmanf is an invertebrate neurotrophic factor supporting dopaminergic neurons

    PNAS 106:2429–2434.

    https://doi.org/10.1073/pnas.0810996106

    • PubMed
    • Google Scholar
69. 1. Polevoy H
    2. Malyarova A
    3. Fonar Y
    4. Elias S
    5. Frank D

    (2017) Foxd1 protein interacts with Wnt and BMP signaling to differentially pattern mesoderm and neural tissue

    The International Journal of Developmental Biology 61:293–302.

    https://doi.org/10.1387/ijdb.160300df

    • PubMed
    • Google Scholar
70. 1. Qiu X
    2. Hill A
    3. Packer J
    4. Lin D
    5. Ma YA
    6. Trapnell C

    (2017a) Single-cell mrna quantification and differential analysis with census

    Nature Methods 14:309–315.

    https://doi.org/10.1038/nmeth.4150

    • PubMed
    • Google Scholar
71. 1. Qiu X
    2. Mao Q
    3. Tang Y
    4. Wang L
    5. Chawla R
    6. Pliner HA
    7. Trapnell C

    (2017b) Reversed graph embedding resolves complex single-cell trajectories

    Nature Methods 14:979–982.

    https://doi.org/10.1038/nmeth.4402

    • PubMed
    • Google Scholar
72. 1. Richman C
    2. Rashid S
    3. Prashar S
    4. Mishra R
    5. Selvaganapathy PR
    6. Gupta BP

    (2018) C. elegans MANF homolog is necessary for the protection of dopaminergic neurons and ER unfolded protein response

    Frontiers in Neuroscience 12:544.

    https://doi.org/10.3389/fnins.2018.00544

    • PubMed
    • Google Scholar
73. 1. Richter JN
    2. Hochner B
    3. Kuba MJ

    (2016) Pull or push? octopuses solve a puzzle problem

    PLOS ONE 11:e0152048.

    https://doi.org/10.1371/journal.pone.0152048

    • PubMed
    • Google Scholar
74. 1. Romani S
    2. Campuzano S
    3. Modolell J

    (1987) The achaete-scute complex is expressed in neurogenic regions of Drosophila embryos

    The EMBO Journal 6:2085–2092.

    https://doi.org/10.1002/j.1460-2075.1987.tb02474.x

    • PubMed
    • Google Scholar
75. 1. Satpute-Krishnan P
    2. DeGiorgis JA
    3. Conley MP
    4. Jang M
    5. Bearer EL

    (2006) A peptide zipcode sufficient for anterograde transport within amyloid precursor protein

    PNAS 103:16532–16537.

    https://doi.org/10.1073/pnas.0607527103

    • PubMed
    • Google Scholar
76. 1. Schnell AK
    2. Smith CL
    3. Hanlon RT
    4. Harcourt RT

    (2015) Female receptivity, mating history, and familiarity influence the mating behavior of cuttlefish

    Behavioral Ecology and Sociobiology 69:283–292.

    https://doi.org/10.1007/s00265-014-1841-5

    • Google Scholar
77. 1. Shigeno S
    2. Parnaik R
    3. Albertin CB
    4. Ragsdale CW

    (2015) Evidence for a cordal, not ganglionic, pattern of cephalopod brain neurogenesis

    Zoological Letters 1:26.

    https://doi.org/10.1186/s40851-015-0026-z

    • PubMed
    • Google Scholar
78. 1. Skeath JB
    2. Carroll SB

    (1992) Regulation of proneural gene expression and cell fate during neuroblast segregation in the Drosophila embryo

    Development 114:939–946.

    https://doi.org/10.1242/dev.114.4.939

    • PubMed
    • Google Scholar
79. 1. Skeath JB
    2. Doe CQ

    (1996) The achaete-scute complex proneural genes contribute to neural precursor specification in the Drosophila CNS

    Current Biology 6:1146–1152.

    https://doi.org/10.1016/s0960-9822(02)70681-7

    • PubMed
    • Google Scholar
80. 1. Soares DS
    2. Homem CCF
    3. Castro DS

    (2022) Function of proneural genes ascl1 and asense in neurogenesis: how similar are they?

    Frontiers in Cell and Developmental Biology 10:838431.

    https://doi.org/10.3389/fcell.2022.838431

    • PubMed
    • Google Scholar
81. Preprint

    1. Songco-Casey JO
    2. Coffing GC
    3. Piscopo DM
    4. Pungor JR
    5. Kern AD
    6. Miller AC

    (2022) Cell Types and Molecular Architecture of the Octopus Visual System

    bioRxiv.

    https://doi.org/10.1101/2022.06.11.495763

    • Google Scholar
82. 1. Staudinger MD
    2. Buresch KC
    3. Mäthger LM
    4. Fry C
    5. McAnulty S
    6. Ulmer KM
    7. Hanlon RT

    (2013) Defensive responses of cuttlefish to different teleost predators

    The Biological Bulletin 225:161–174.

    https://doi.org/10.1086/BBLv225n3p161

    • PubMed
    • Google Scholar
83. 1. Street K
    2. Risso D
    3. Fletcher RB
    4. Das D
    5. Ngai J
    6. Yosef N
    7. Purdom E
    8. Dudoit S

    (2018) Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

    BMC Genomics 19:477.

    https://doi.org/10.1186/s12864-018-4772-0

    • PubMed
    • Google Scholar
84. 1. Stuart T
    2. Butler A
    3. Hoffman P
    4. Hafemeister C
    5. Papalexi E
    6. Mauck WM
    7. Hao Y
    8. Stoeckius M
    9. Smibert P
    10. Satija R

    (2019) Comprehensive integration of single-cell data

    Cell 177:1888–1902.

    https://doi.org/10.1016/j.cell.2019.05.031

    • PubMed
    • Google Scholar
85. 1. Styfhals R
    2. Zolotarov G
    3. Hulselmans G
    4. Spanier KI
    5. Poovathingal S
    6. Elagoz AM
    7. De Winter S
    8. Deryckere A
    9. Rajewsky N
    10. Ponte G
    11. Fiorito G
    12. Aerts S
    13. Seuntjens E

    (2022) Cell type diversity in a developing octopus brain

    Nature Communications 13:7392.

    https://doi.org/10.1038/s41467-022-35198-1

    • PubMed
    • Google Scholar
86. 1. Sur A
    2. Renfro A
    3. Bergmann PJ
    4. Meyer NP

    (2020) Investigating cellular and molecular mechanisms of neurogenesis in capitella teleta sheds light on the ancestor of annelida

    BMC Evolutionary Biology 20:84.

    https://doi.org/10.1186/s12862-020-01636-1

    • PubMed
    • Google Scholar
87. 1. Sweedler JV
    2. Li L
    3. Floyd P
    4. Gilly W

    (2000) Mass spectrometric survey of peptides in cephalopods with an emphasis on the fmrfamide-related peptides

    The Journal of Experimental Biology 203:3565–3573.

    https://doi.org/10.1242/jeb.203.23.3565

    • PubMed
    • Google Scholar
88. 1. Sweedler JV
    2. Li L
    3. Rubakhin SS
    4. Alexeeva V
    5. Dembrow NC
    6. Dowling O
    7. Jing J
    8. Weiss KR
    9. Vilim FS

    (2002) Identification and characterization of the feeding circuit-activating peptides, a novel neuropeptide family of aplysia

    The Journal of Neuroscience 22:7797–7808.

    https://doi.org/10.1523/JNEUROSCI.22-17-07797.2002

    • PubMed
    • Google Scholar
89. 1. Takeuchi K
    2. Satou Y
    3. Yamamoto H
    4. Satoh N

    (2005) A genome-wide survey of genes for enzymes involved in pigment synthesis in an ascidian, Ciona intestinalis

    Zoological Science 22:723–734.

    https://doi.org/10.2108/zsj.22.723

    • PubMed
    • Google Scholar
90. 1. Thøgersen IB
    2. Salvesen G
    3. Brucato FH
    4. Pizzo SV
    5. Enghild JJ

    (1992) Purification and characterization of an alpha-macroglobulin proteinase inhibitor from the mollusc octopus vulgaris

    The Biochemical Journal 285 (Pt 2):521–527.

    https://doi.org/10.1042/bj2850521

    • PubMed
    • Google Scholar
91. 1. Thompson JT
    2. Kier WM

    (2001) Ontogenetic changes in fibrous connective tissue organization in the oval squid, Sepioteuthis lessoniana lesson, 1830

    The Biological Bulletin 201:136–153.

    https://doi.org/10.2307/1543329

    • PubMed
    • Google Scholar
92. 1. Tomarev SI
    2. Piatigorsky J

    (1996) Lens crystallins of invertebrates -- diversity and recruitment from detoxification enzymes and novel proteins

    European Journal of Biochemistry 235:449–465.

    https://doi.org/10.1111/j.1432-1033.1996.00449.x

    • PubMed
    • Google Scholar
93. 1. Trapnell C
    2. Cacchiarelli D
    3. Grimsby J
    4. Pokharel P
    5. Li S
    6. Morse M
    7. Lennon NJ
    8. Livak KJ
    9. Mikkelsen TS
    10. Rinn JL

    (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

    Nature Biotechnology 32:381–386.

    https://doi.org/10.1038/nbt.2859

    • PubMed
    • Google Scholar
94. 1. van Giesen L
    2. Kilian PB
    3. Allard CAH
    4. Bellono NW

    (2020) Molecular basis of chemotactile sensation in octopus

    Cell 183:594–604.

    https://doi.org/10.1016/j.cell.2020.09.008

    • PubMed
    • Google Scholar
95. 1. Wardill TJ
    2. Gonzalez-Bellido PT
    3. Crook RJ
    4. Hanlon RT

    (2012) Neural control of tuneable skin iridescence in squid

    Proceedings. Biological Sciences 279:4243–4252.

    https://doi.org/10.1098/rspb.2012.1374

    • PubMed
    • Google Scholar
96. 1. Wild E
    2. Wollesen T
    3. Haszprunar G
    4. Heß M

    (2015) Comparative 3D microanatomy and histology of the eyes and central nervous systems in coleoid cephalopod hatchlings

    Organisms Diversity & Evolution 15:37–64.

    https://doi.org/10.1007/s13127-014-0184-4

    • Google Scholar
97. 1. Wollesen T
    2. Cummins SF
    3. Degnan BM
    4. Wanninger A

    (2010) Fmrfamide gene and peptide expression during central nervous system development of the cephalopod mollusk, idiosepius notoides

    Evolution & Development 12:113–130.

    https://doi.org/10.1111/j.1525-142X.2010.00398.x

    • PubMed
    • Google Scholar
98. 1. Wollesen T
    2. Sukhsangchan C
    3. Seixas P
    4. Nabhitabhata J
    5. Wanninger A

    (2012) Analysis of neurotransmitter distribution in brain development of benthic and pelagic octopod cephalopods

    Journal of Morphology 273:776–790.

    https://doi.org/10.1002/jmor.20023

    • PubMed
    • Google Scholar
99. 1. Young JZ

    (1965) The croonian lecture, 1965 - the organization of a memory system

    PNAS 163:285–320.

    https://doi.org/10.1098/rspb.1965.0071

    • Google Scholar
100. Website

     1. Young RE

     (1973) Information Feedback from Photophores and Ventral Countershading in Mid-Water Squid

     Accessed November 14, 2022.

     http://hdl.handle.net/10125/778
101. 1. Young JZ

     (1976) The nervous system of Loligo. II. suboesophageal centres

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 274:101–167.

     https://doi.org/10.1098/rstb.1976.0041

     • PubMed
     • Google Scholar
102. 1. Young JZ

     (1985) Cephalopods and neuroscience

     The Biological Bulletin 168:153–158.

     https://doi.org/10.2307/1541328

     • Google Scholar
103. 1. Young JZ

     (1991) Computation in the learning system of cephalopods

     The Biological Bulletin 180:200–208.

     https://doi.org/10.2307/1542389

     • PubMed
     • Google Scholar
104. 1. Zhang Y
     2. Mao F
     3. Mu H
     4. Huang M
     5. Bao Y
     6. Wang L
     7. Wong N-K
     8. Xiao S
     9. Dai H
     10. Xiang Z
     11. Ma M
     12. Xiong Y
     13. Zhang Z
     14. Zhang L
     15. Song X
     16. Wang F
     17. Mu X
     18. Li J
     19. Ma H
     20. Zhang Y
     21. Zheng H
     22. Simakov O
     23. Yu Z

     (2021) The genome of Nautilus pompilius illuminates eye evolution and biomineralization

     Nature Ecology & Evolution 5:927–938.

     https://doi.org/10.1038/s41559-021-01448-6

     • PubMed
     • Google Scholar
105. 1. Zhao G
     2. Boekhoff-Falk G
     3. Wilson BA
     4. Skeath JB

     (2007) Linking pattern formation to cell-type specification: dichaete and IND directly repress achaete gene expression in the Drosophila CNS

     PNAS 104:3847–3852.

     https://doi.org/10.1073/pnas.0611700104

     • PubMed
     • Google Scholar

Author details

1. Jules Duruz

   Department of Biology, Institute of Zoology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1860-9546

2. Marta Sprecher

   Department of Biology, Institute of Zoology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

3. Jenifer C Kaldun

   Department of Biology, Institute of Zoology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

4. Al-Sayed Al-Soudy

   Department of Biology, Institute of Zoology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7830-9660

5. Heidi EL Lischer

   Interfaculty Bioinformatics Unit and Swiss Institute of Bioinformatics, University of Bern, Bern, Switzerland

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9616-2092

6. Geert van Geest

   Interfaculty Bioinformatics Unit and Swiss Institute of Bioinformatics, University of Bern, Bern, Switzerland

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1561-078X

7. Pamela Nicholson

   Institute of Genetics, University of Bern, Bern, Switzerland

   Contribution

   Investigation, Methodology, Project administration

   Competing interests

   No competing interests declared

8. Rémy Bruggmann

   Interfaculty Bioinformatics Unit and Swiss Institute of Bioinformatics, University of Bern, Bern, Switzerland

   Contribution

   Investigation, Methodology, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4733-7922

9. Simon G Sprecher

   Department of Biology, Institute of Zoology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - original draft, Project administration

   For correspondence

   simon.sprecher@unifr.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9060-3750

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