NeuroSystematics and Periodic System of Neurons:

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NeuroSystematics and Periodic System of Neurons: Model vs Reference Species at Single-cell Resolution Leonid Moroz ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00100 • Publication Date (Web): 10 Jul 2018 Downloaded from http://pubs.acs.org on July 11, 2018

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ACS Chemical Neuroscience

NeuroSystematics and Periodic System of Neurons: Model vs. Reference Species at Single-cell Resolution

Leonid L. Moroz

Department of Neuroscience and McKnight Brain Institute, University of Florida, 1149 Newell Drive, Gainesville, FL 32611, USA Whitney Laboratory for Marine Bioscience, University of Florida, 9505 Ocean Shore Blvd., St Augustine, FL 32080, USA

Keywords: Single-cell sequencing (scRNA-seq); Parallel Evolution of Neurons and Synapses; Ctenophores and Basal Metazoa; Bilaterian Brains; Neuronal Taxonomy and Cell Atlas; Trichoplax, Xenoturbella.

Acknowledgments: This work was supported by the United States National Aeronautics and Space Administration (grant NASA-NNX13AJ31G), the National Science Foundation (grants 1146575, 1557923, 1548121 and 1645219) and National Institute of Health (grants R01GM097502, R01MH097062-01A1).

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Abstract There is more than one way to develop neuronal complexity, and animals frequently use different molecular toolkits to achieve similar functional outcomes (=convergent evolution). Neurons are different not only because they have different functions, but also because neurons and circuits have different genealogies, and perhaps independent origins at the broadest scale from ctenophores and cnidarians to cephalopods and primates. By combining modern phylogenomics, single-neuron sequencing (scRNA-seq), machine learning, single-cell proteomics and metabolomic across Metazoa, it is possible to reconstruct the evolutionary histories of neurons tracing them to ancestral secretory cells. Comparative data suggest that neurons, and perhaps synapses, evolved at least three times (in ctenophore, cnidarian and bilateral lineages) during ~600 million years of animal evolution. There were also several independent events of the nervous system centralization either from a common bilateral/cnidarian ancestor without the bona fide neurons or from the urbilaterian with diffuse, nerve-net type nervous system. From the evolutionary standpoint, (i) a neuron should be viewed as a functional rather than a genetic character, and (ii) any given neural system might be chimeric and composed of different cell lineages with distinct origins and evolutionary histories. The identification of distant neural homologies or examples of convergent evolution among 34 phyla will not only allow the reconstruction of neural systems’ evolution but together with single-cell ‘omic’ approaches the proposed synthesis would lead to the ‘Periodic System of Neurons’ with predictive power for neuronal phenotypes and plasticity. Such a phylogenetic classification framework of Neuronal Systematics (NeuroSystematics) might be a conceptual analog of the Periodic System of Chemical Elements. scRNA-seq profiling of all neurons in an entire brain or Brain-seq is now fully achievable in many nontraditional reference species across the entire animal kingdom. Arguably, marine

animals are the most suitable for the proposed tasks because the world oceans represent the greatest taxonomic and body-plan diversity.


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Short Abstract. The brain is one of the most complex structures in the known universe, and virtually every neural system consists of a tremendous diversity of neuronal phenotypes. How and why neurons are so different is one of the most fundamental questions in neuroscience. However, we know very little about molecular/genomic mechanisms of neuronal identity and relationships between different neuronal subtypes and between species. Our major hypothesis states that neurons are different not only because they have different functions but also because they have different origins from genetically and developmentally distinct ancestral cellular lineages. In other words, neurons have different genealogies. Moreover, neurons as many other cell types in Metazoa might act as distinct evolutionary units capable of maintaining a core regulatory machinery and mechanisms underlying the preservation of their identities over millions of years. Thus, recognizing even distant cell homologies across taxa may be possible. That requires molecular/genomic sampling of multiple cells from a broad array of close and more distant related species within ALL animal clades. Arguably, marine animals are the most suitable for the proposed tasks because the world oceans represent the greatest taxonomic and body-plan diversity.



“In some sense, the human brain is like a museum containing remnants of all the previous stages in our evolution over millions of years, exploding outward and forward in size and function.” - The future of the mind 1

Introduction The 21st century might well be the century of the BRAIN with the primary objective to decipher the mechanisms and logic of neural functions as a roadmap to the synthetic neuroscience of the future. Conceptually, how could novel types of neurons, circuits, and even brains, some of which might not exist in nature, be made? The induced pluripotent stem cells2, trans-differentiation or gene editing have been already explored as a potential cure for neurodegenerative diseases and memory dysfunctions or to enhance cognitive capabilities. But the tremendous diversity of neuronal phenotypes and connectivity is the major obstacle in this direction. Thus, the census of neuronal cell types and subsequent construction of the comprehensive brain atlases across species (Fig. 1), and eventually natural unbiased neuronal classification are critical milestones in neuroscience today. We are at the very beginning of a long-term, but exciting an exciting endeavor.

Classification is the alpha and omega of Neuroscience. What separates biology from other sciences is the tremendous diversity of life. More than 100 billion species had lived on our planet with ~10 million extant species3. The underappreciated diversity of life offers multiple examples of parallel and convergent evolution – mother Nature’s solutions, where phenotypically similar structures or functions can be achieved using different molecular toolkits and mechanisms. In other words, diverse molecular players and mechanisms evolved independently as adaptations to similar environments and, perhaps,


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under similar physical or chemical constraints4-12. What are these constraints? We still do not know; and we cannot predict cellular/neurons and organismal phenotypes from primarily genomic information. We have very limited information about the actual diversity of species and diversity of neurons across the animal kingdom. Out of about 34-35 phyla (Fig. 7), only six of them were subjects of systematic neurobiological studies: Chordata, Arthropoda (primarily Drosophila melanogaster), Nematoda (primarily Caenorhabditis elegans – there are more neuroscientists, who study this model, then actual neurons in the worm), Annelida, Mollusca, Cnidaria. The rest of phyla are barely touched by neuroscientists. And, in spite of many hundred thousands of human genomes sequenced so far, for most of the animal phyla we still do not have well-annotated genomes. The megadiversity of life is the results of >3.5 billion years of biological evolution. Such a long history provides substantial challenges to the inventory of organisms, cells, developmental pathways, and genes. At the same time, I view the existing biodiversity as a true gift to biologists, offering unique experiments performed by Nature, which can help us identify constraints both in organismal and brain organizations. Many parallels exist between the evolution of organisms and evolution of cell types within the concept of adaptive space: some solutions or phenotypes are more favored by natural selection and possible, whereas some outcomes are not competitive and, therefore are not ‘permissible.’ Ideally, any natural biological classification, including the classification of neurons should reflect both genealogical relationships (=Homology) and examples of convergent and parallel evolution. However, evolutionary classification does not exist for any neuronal cell lineages (or any other cell types in Metazoa), mostly because the identification of neuronal homologies (or lack of them) is a very laborious task.



At the level of individually identified neurons - cell type homologization is virtually absent, with a few exceptions from selected gastropod mollusks13, 14. Thus, the establishing of sister-group/phylogenetic relationships at the level of specific cell populations are essential components to understand the nature of the brain organization and the origin of neuronal novelties. There are also historical parallels between studies to understand biodiversity and approaches to understand the brain15 - the transformation from the descriptive taxonomy to the genealogical classification of organisms or neurons. In fact, it took over 250 years in biosystematics to transition from amateurs’ collections of species in 1700-1900s to the ongoing phylogenomics of all domains of Life3, which only started during the last two decades with advents of sequencing technologies. Arguably, the dawn of the modern biology began with the Linnaeus nomenclature of organisms – the twelve editions of Systema Naturae (1735-1768). Although imperfect by today’s standards, this binomial classification served as a catalyst, providing the solid foundation for all major divisions of biosciences. Carl Linnaeus grouped organisms as a series of hierarchical taxonomical units starting with species, genera, families, orders, classes, and phyla. Thus, he unknowingly provided the system, which eventually coupled his static and descriptive classification to the Darwinian, evolutionary origin of species, and the genealogy of Life from a common ancestor. The present shape of the tree of Life incorporates a remarkable predictive power toward organismal phenotypes. To a certain degree, by collecting species within a given family or genus, a taxonomist might develop reasonable predictions about internal anatomy or gene complements within the studied group.


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The whole history of biology, as a scientific discipline, began with the analysis of biodiversity. Biodiversity started with biogeography, and later the classification approaches were naturally expanded to anatomy, histology, physiology, biochemistry, and genomics. All biological disciplines started with some classification, often partially artificial, such as the classification of metabolites and enzymes in biochemistry or the current gene ontology (GO) in molecular biology. Genomic tools can resolve genealogical relationships not only among animal lineages but also among genes, proteins, complex molecular structures, organelles and eventually cells. However, the genealogical classification of animal cell types16, including neurons17, is still the terra incognita. In fact, the lack of comparative cell-specific gene expression data across close and more distant taxonomical groups prevents building both theoretical models and limits practical steps to reconstruct ancestral neuronal lineages. The large-scale BRAIN initiative (Brain Research through Advancing Innovative Neurotechnologies®) has little attention to evolutionary approaches. Admittedly, the human brain is one of the most complex structures in the known universe – it consists of ~86 billion neurons and about the same number of non-neuronal cells – many of which are unique regarding their connectivity, molecular organization, and plasticity mechanisms. There are ~100 trillion synapses, and many of them are also quite distinct in both functional and molecular properties. Not surprisingly, much greater diversity of neuronal types and synapses is observed across species, especially among invertebrates. Every model organism or neural circuit studied in sufficient detail (e.g., the nematode Caenorhabditis elegans, the fruit fly – Drosophila melanogaster, the sea slug Aplysia californica, the medicinal leech Hirudo medicinalis or the stomatogastric ganglion in crabs and lobsters) point out to one major conclusion: all neurons are



remarkably different! It would be safe to say that in the abovementioned ‘model’ species deep molecular analyses of any identified cell populations indicated that neurons are also unique in their genomic/transcriptional profiles18, 19. But how are these neuronal populations related to each other? How can we transfer data from one species to another? The Schwann’s and Schleiden’s cell theory (1838-1839) established that a cell is the fundamental unit of life. But, only with more than 50 years ‘delay’, the Ramón y Cajal neuron doctrine followed, which also established that a neuron [as a cell, not as a syncytium] is the only principal unit of any neural system20.

Using advances of single-cell genomics, we start

clustering and initial classification of neurons both in a given species (as in the human/brain cell atlas initiatives21-26), and, equally important, evolutionary across classes27 and phyla. Eventually (in the 21st century), all metazoan cells, neuronal types, and states would be characterized molecularly. The emerging unbiased evolutionary classification of neurons would be the alpha and omega of future neuroscience. Here, I will outline some challenges and critical points towards the natural classification of neurons, or NeuroSystematics, focusing on the comparative framework. In contrast to the progress in the phylogenomics of animals, NeuroSystematics, and even an initial census of brain cell types (vs. cell states – see boxes 1 & 2) is still in its infancy.

How many neurons and glia in the brain and how different are they? Let’s start with the “simplest” question: How many neurons vs. other cell types present in a mammalian brain? The glia/neuron ratio can vary substantially among species. At least in part, the ration is coupled with decreasing neuronal density due to increasing average neuronal cell size, and metabolic constraints during ~65 million years of mammalian adaptive radiation. The


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observed disparities in the glia/neuron ratio are related to the large variation in neuronal sizes across brain structures and species compared to less overall variation in glial cell sizes with significant constraints in the brain organization and function28. In last few years, it became possible to resolve controversies and determine a true number of neurons vs. glia cells in humans29. The widely discussed number of 100-120 trillion synapses in the human brain are just rough estimates. We know less about the neuronal and synaptic census in other vertebrates and very little about invertebrates, with a few exceptions. From the comparative perspective, there is not too much extraordinarily in the human brain: it is a linearly scaled-up primate brain both in its cellular composition and metabolic cost30. The most recent counts revealed that ~1.5 kg adult male human brain consists of ~86 a billion neurons, ~85 billion non-neuronal cells (glia and others) or ~ a 1:1 overall neurons/glia ratio31. There are ~100 million neurons in the spinal cord, and ~500 million in the enteric nervous system, but precise counts are still required (for primates see32). Our astonishing cognitive abilities are located in a relatively enlarged cerebral cortex when scaled to other primates, with ~16 billion neurons or just 19% of the total neurons in the adult brain30. But humans do not hold all records. The long-finned pilot whale or large oceanic dolphin, Globicephala melas, has 29-46 billion neocortical neurons and up to 183 billion glial cells (see33, although the total number of neurons is unknown). The elephant brain has about three times as many cells as the average human: ~257 billion neurons vs. 216 billion glial and other cell types34. Most of them, or 251 billion elephant neurons and 38 billion other cells, are found in the cerebellum; and ‘only’ 5.6 billion in the cortex with 150 billion of glia and others cell classes. Thus, at a large phyletic scale, there is a loose correlation between the absolute number of cortical neurons, glia, and



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cognitive capabilities, but such correlations are more obvious within a particular animal lineage such as primates or cetaceans or carnivores rather than across evolutionarily distant groups. The larger brains of cetaceans and elephants have less overall neuronal density, especially in the cortex, and is due to an increase in average neuronal cell size, and a relatively larger proportion of glial cells to neurons. In contrast, glia is not the most numerous cell population in the brains of mice and rats (~40%), which contain about 71 and 200 million neurons respectively35, 36. In summary, although neuronal density varies dramatically, there are no systematic variations in the density of non-neuronal cells across mammalian brains and structures, suggesting conservative evolutionary mechanisms to control development and small glia sizes over more than 100 million years. Can predators be ‘smarter’ than their preys and, therefore, have a more complex brain? Regarding the number of neurons, there are no obvious differences between predators and preys of the same size, although wiring might be altered. For a given cortical size, carnivores have comparable numbers of neurons as the herbivore species they prey upon37.

Similarly,

domesticated animals such as chicken, pigs, dogs, and cats are not so different from wild relatives in the allometric scaling of their brains.37, 38. The most provocative results were reported for cats and dogs, which possess 1.2 and 2.2 billion of neurons, respectively37. This study reopens old debates: which of our pets is “smarter,” and what is the relationship between the number of neurons and various brain functions including cellular bases of the elementary cognition, learning, and memory. Unfortunately, even for these two fully accessible species, systematical comparisons of cognition and behaviors as well as their relationships to quantitative high-resolution neuroanatomy have not been performed.


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As always in comparative biology, a few important “exceptions” were discovered during 10-year surveys of cellular brain compositions. Carnivorans have similar relationships between larger body, larger cortical mass and a larger number of cortical neurons as other non-primate species, but such evolutionary scaling only applies to small and medium-sized species, and not beyond dogs. In 2017, the Herculano-Houzel team found that the golden retriever dog has more cortical neurons than the striped hyena, African lion, and brown bear, even though the latter species have up to three times larger cortices than dogs. Intriguingly, “the brown bear cerebral cortex, the largest species examined in this study, has as many neurons as the ten times smaller cat cerebral cortex, although it does have the expected ten times more non-neuronal cells in the cerebral cortex compared to the cat”37. Similarly, “raccoons have dog-like numbers of neurons in their cat-sized brain, which makes them comparable to primates in neuronal density” 37. Notably, rather than direct “number of neurons-to-intelligence” causality, constraints of bioenergetics/high metabolic cost might be a key factor in the brain size growth evolution. In carnivores as in primates/ape/human lineages, the energetic cost is proportional to the number of neurons. Regardless of brain size, cortex neurons consume ten times as much energy as neurons in the cerebellum39. In other words, the large and behaviorally active carnivorans have the cortex that is the most energetically expensive part of the brain. The cortex can be particularly vulnerable to metabolic constraints that impose a trade-off between body size and a number of cortical neurons37. At least in part, it might be compensated by a daily sleep requirement for large carnivores, which might sleep as much as 12 hours per day40. On the other hand, more neurons in mammals often mean smaller neurons. Similar relationships have been observed in invertebrates.



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The “primate of the sea” Octopus holds the record in the brain cell number among invertebrates with ~500 million neurons41 or at least seven times more neurons than in the mice brain35, 36, 42 (glial cells have not been characterized in cephalopods). Most of Octopus’s neurons are tiny, few micrometers, amacrine cells in the vertical lobes, also known as parts of distributed memory centers, plus a diversity of predominantly small neurons in optic lobes and arm neural cords43-46. On another end of the spectrum in neuronal numbers are microscopic invertebrate larvae, dwarf species of meiofauna or parasites – many of these poorly explored organisms have less than 30-200 neurons per an entire animal. The smallest number of neurons across free-living animals was found in males of the annelid Dinophilus. This miniature worm contains only 66 neurons and just two glia cells47 but expressed a remarkable array of behaviors including the copulation. Thus, with the range of 102 - 1011 neurons per brain we expect to discover multiple independent examples of neuronal gains and losses across species. Similarly, there are at least three orders of magnitude differences in the neuronal sizes across phyla. Some of the smallest neurons, 1-3 µm cell diameter, are found in C. elegans and dwarf annelids as well as in many larvae. The largest neurons in Metazoa were discovered in Aplysia, Pleurobranchaea, Tritonia, and related gastropod species48 – all of them contain ‘only’ about 10,000-20,000 neurons in their central nervous systems (CNSs). Cholinergic/peptidergic motoneurons neurons, such as R2 in Aplysia, reach 1.1 mm in size49 and some growth cones reach 632 µm49, 50, also making them the largest growth cones ever reported in the animal kingdom. The gigantic neurons of Aplysia are highly polyploid with the number of copies of DNA 120,000-250,000 per neuron49, another “Guinness book’ record for animals.


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In contrast, the smallest flying insects of the parasitic wasp genus Megaphragma (Hymenoptera: Trichogrammatidae) evolve anucleate adult neurons53 and yet exhibit complex behaviors such as flight and search for preys. In fact, the adult body length of these insects is so small (e.g., 170 µm in M. caribea, 200-250 µm in M. mymaripenne and up to 280 µm in M. amalphitanum54) that several whole wasps could be placed inside one Aplysia R2 or LPl1 motor neurons. Interestingly, Megaphragma pupae have typical, for the insects, ganglia with ~7,400 nucleated cells, over 95% of them consequently lost nuclei and even lyse neuronal cell bodies with preservation of neuronal processes (the neuropil) during development and metamorphosis. Miniaturization of neurons, in concert with the miniaturization of other features, might also occur independently as adaptations to unique environments53,

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. In general, the smallest

insects also have significantly reduced numbers of neurons compared to their close and more distant relatives. For example, the smallest beetles of the family Ptiliidae (Coleoptera) have about 40,000 cells in their nervous systems57 vs. about 340,000 neurons in the house fly (Musca), ~850,000 neurons in the honey bee (Apis) workers and one million neurons in cockroaches58, 59. Finally, brain allometric scaling, when applied to invertebrates, and insects in particular, also provided some surprises. The human brain weights ~2.5% of the body mass and the cephalization/cerebral index among vertebrates can be the highest in hummingbirds - 8.33%. However, brain/body index reaches 8.36% in the miniature hymenopteran insect Trichogramma, 11.95% in first-instar nymphs of the psocopteran Liposcelis, and 15% in Brachymyrmex, some of the smallest ants54,

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. Very small spiders also resemble other small animals in having

disproportionately larger central nervous systems (CNSs) relative to body mass when compared with large-bodied forms - the relatively large CNS of a very small spider occupies up to 78% of the cephalothorax volume61. Ecological, developmental and body-plan constrains enormously



shape neural systems resulting in numerous unique molecular and systemic solutions for dynamic operations of neural circuits and behaviors.

Evolutionary approaches are critical to decipher brain mechanisms and classify neurons Such puzzling complexities and diversities of nervous systems across structure and species constantly drive the development of novel technologies and ideas to capture the operations of the brain as a whole system at the single-cell resolution. Ongoing multi-billion dollars BRAIN initiatives62,

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are eventually asking: how more than 170 billion human brain

cells work together to generate stereotyped and learned behaviors. What types of neurons and neural pathways compose the brain? Two initial projects are underway to define the cell census in the mouse and marmoset brains as a starting point, in parallel to zebrafish24, 26 and reptilian27 studies. In the 1790s, Galvani and Volta pioneered one of the most well-known experimental paradigms to explain brain mechanisms. This paradigm emphasizes unidirectional or primarily electrical neuronal signaling and views brains as electrical machines. The very name of neuronal circuits is derived from electrical terminology and subsequently highlighted in computer schemes. For example, one of the corollaries of the electrical paradigm: once the precise wiring or connectome would be decoded, the understanding of cellular bases of behavior and neuropathologies could be achieved.


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Reflex circuits and central pattern generators are successful illustrations of important milestones within this ‘electrical’ framework. However, the concept of precisely wired circuits is not the case for all, even numerically simpler nervous systems. Neurons, signal molecules, and neurotransmitters are extraordinarily different from the chemical standpoint. Everywhere, when experimentally tested, the emerging molecular and genomic diversity of neurons significantly exceed any imaginable electrical circuit, even in C. elegans64. The classical electrical circuit archetype is rapidly transforming into the chemical paradigm of neural functions with logic and approaches of quantitative microchemistry at the nanoscale resolution. In fact, at the first approximation, virtually all neural systems are peptidergic with hundreds of signal molecules acting together with volume transmission mediated by classical low molecular weight and gaseous messengers such as nitric oxide, carbon dioxide, and H2S. The brain, like any other biological system, is complex multimolecular machinery with 100-500 billion different cellular players. Every second and millisecond, millions of neurons, glia, immune and circulatory cells interact both using synapses and, importantly, non-synaptic volume transmission (including signaling peptides and gases). Each cell (or synapse), is individualized, perhaps unique, in its molecular composition, development and energetic requirements. To support the integrative activity of the nervous system >100,000 different RNAs65 and hundreds of covalent RNA modifications66-71 operate in each neuron. As an output, thousands of clusters of signaling/secretory molecules are simultaneously transmitting information across the brain at the nanoscale resolution. The scope of interacting proteins and metabolites in any neuron is unknown, and how 100 trillion of synaptic connections find each other during the human brain development still eludes us.



The principles and mechanisms of neuronal individuality and brain-wide coordinated plasticity are unknown for the ‘simpler’ Drosophila brain with the “only” ~150,000-200,000 neurons and ~10 million connections, or even for the ‘simplest’ network of 302 Caenorhabditis elegans neurons with ~7,500 synapses identified by electron microscopy72. We do not even know the genomic blueprint of each of these 302 identified neurons in C. elegans. It is well-recognized that developing novel technologies for massive parallel single-cell genomics73-76, proteomics77, lipidomics78, and metabolomics79, as well as real-time imaging of molecular processes in the nucleus, cytoplasm synaptic processes, and synaptic cleft, are needed to characterize the chemical heterogeneity of every neuron at least in each of the model organisms outlined in Fig. 1. Many breakthroughs have been achieved in each of these directions. This path alone is not the cost-efficient solution to decipher the puzzling complexity of the brain. The first questions are: How similar are neurons to each other and among species? How can we unbiasedly classify them? Reemphasizing the most famous motto80 - Nothing in Neuroscience makes sense except in the light of Evolution. The scope of the extant diversity of neurons is largely unknown, and the growing single-cell molecular data confirm the uniqueness of thousands of neurons and other cells sequenced73, 76, 81-83. We need to know not only how neurons are different, but also why they are so different? We need to know not only how many signaling molecules operate in the brain and how different are (neuro)transmitters; but also, why there are so many transmitters instead of one excitatory and one inhibitory transmitter13, 14, 17. How this diversity is originated and evolved in the first place, and what constraints and “opportunities” of the ancestral molecular organization might exist leading to the different brains of a fly, worm, octopus or human. Many answers to these and other questions related to neuronal identity and plasticity are dependent


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upon our understanding of the origins and scope of molecular innovations supporting the parallel evolution of neural systems. The discovered molecular complexity of the numerically “simpler” neural systems and the emerging single-cell data suggest the hypothesis (Figs. 2, 3): neurons are different not only because they have different functions, but also because neurons and circuits have different genealogies, and perhaps independent origins at the broadest evolutionary scale84-86.

Model vs. Reference species in Neuroscience and Biology Usage of human cells or a few model species even as an overall strategy to cure mental and other neurological disorders would not be sufficient. Without the knowledge of the evolutionary history of neuronal lineages, it might be difficult to understand why, for example, human cholinergic neurons are so sensitive to degeneration associated to Alzheimer’s disease, whereas in many other species they are quite resistant to stress and aging. With the advent of single-cell genomic and microanalytical technologies, deep molecular and functional surveys of a broad spectrum of unique neural systems across all 34-35 animal phyla should be performed in parallel both in the laboratory and at oceanic sites, which holds the greatest biodiversity on our planet15. A more accurate approach for fundamental neuroscience is the concept of reference species vs. traditional viewpoint of model species, as recommended by the NSF workshop organized at the dawn of the BRAIN initiative87. Figures 3 and 4 show examples of reference species representing all five clades of basal Metazoa. These species also illuminate major



transitions in the formation of neural and synaptic organizations as well as the evolution of memory and elementary cognitions: from diffuse neural nets in selected ctenophores and cnidarians to composite brains in cephalopods, insects, and chordates. In some ways, the history of neuroscience retraces the history of biology with efforts to reconstruct the animal tree of life (from phyla to species) as the framework in the search for distant neuronal homologies or examples of convergent and parallel evolution. First, the field needs at least 34-100 reference species representing all animal phyla recognized today (Fig. 5 and 7). Ideally, it should be several hundred of reference species for Neuroscience, which should represent all classes and key ecological lineages of both land and marine organisms. The usage of marine species in the modern biomedicine is especially desirable since most body-plans occur in oceans, but these organisms are drastically under-investigated today. It might look ambitious, but with the recent reduction of sequencing costs, a network of marine stations and even Ship-seq15 (the opportunity to perform single-cell sequencing directly aboard of oceanic ships), it is a quite achievable task in the nearest future. It can be cheaper to sequence all individual neurons from dozens of reference species than complete the census of a single nervous system from any rodent. Studies on Aplysia19, 88, 89, Drosophila90-92, C. elegans18 and annelids93, 94 revealed that all systematically characterized neurons are unique in their transcription factors, adhesive components, non-coding RNAs and secretory molecules, providing the foundation for the natural evolutionary neuronal classification. The discovered molecular complexity of the numerically “simpler” neural systems always include numerous lineage-specific neuronal innovations in each group of basal metazoans and respective reference species (Figs. 3 and 4). The emerging


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comparative single-cell data are also in concert with the hypotheses of extensive convergent/parallel evolution and chimeric natures of neural systems across phyla84, 95-97.

Independent origins of neurons and synapses The origins of neural systems are one of the major transitions in the evolution of life on our planet, and the growing evidence strongly suggested that neurons and synapses evolved more than once independently13, 14, 17, 84, 85. As with any biological adaptation, the nervous system is a passive product of evolution. Environment drives behavior as a true “pacemaker of evolution”98. As soon as an organism possesses neural and other integrative systems: the adaptive potential, the rate of dispersal and speciation might accelerate significantly, allowing the organism to ‘escape’ from a given environmental constraint. Comb jellies or ctenophores represent the most astonishing example of the extensive convergent evolution in neural systems6, 85, 86. Cilia, not muscles, are primary effectors in most ctenophores, and, perhaps in ancient Precambrian animals. Recent phylogenomic studies on more than 30 species strongly support the hypothesis that Ctenophora is the sister group to the rest of animals85,

99-102

, see also Fig. 3. In this lineage, many canonical animal traits such as

neurons, muscles, and mesoderm, sensory organs, even a gut, and anus might evolve independently from the rest of Metazoa85, 103. Thus, it is not surprising that ctenophores acquired numerous unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of feeding, locomotor and integrative circuits from dozens of ancestral cell lineages. All life in ctenophores is based on ciliated cells and their synaptic and non-synaptic regulations! The polygenesis hypothesis explains the lack of pan-neuronal and pan-synaptic



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genes across metazoans104, including examples of the lineage-specific evolution of neurogenic and signaling molecules as well as synaptic components. Sponges105 and placozoans106 are the only two lineages with the primary absence of neural and muscular systems (Fig. 3). Cnidarians and bilaterians ancestors evolved neurons after their split from the nerveless common ancestor with sponges. And some neuronal lineages in both cnidarians and bilaterians also might evolve independently, but some of them had shared evolutionary history (Fig. 2). It was concluded6, 85, 86 that acetylcholine, serotonin, histamine, dopamine, octopamine, and gamma-aminobutyric acid (GABA) were recruited as transmitters in the neural systems in cnidarian and bilaterian lineages, but most of them are absent in ctenophores as shown by direct microchemical measurements using capillary electrophoresis (see Fig. 5 for example). GABA was detected in ctenophores, but only in muscles6, suggesting that GABA might be a product of glutamate inactivation and muscular bioenergetics. Ctenophores, independently from the rest of Metazoa, evolved numerous secretory and signaling peptides85,

86

. This finding indicates

extensive adaptations within the clade, suggests that early neural systems might be peptidergic6, 84

, and support the idea that neurons evolved from several ancestral secretory cell lineages (Fig.

6B). It is still unclear when and how neural systems originated in the first place (see a historical overview in17). The most popular earlier hypothesis (Fig. 6A), about the origin of neurons from cnidarian type myoepithelial cells, was summarized by George Macki107, 108. At that time ctenophores were less studied and often grouped with Cnidaria as the single clade Coelenterata (the current animal phylogeny does not support this placement). Predation might be


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one of the driving factors in the subsequent accelerated and highly divergent evolution of neural features109. In 2009, it was suggested that injury, stress and subsequent secretion, together with compensatory regeneration mechanisms, might be one of the key pre-adaptations leading to polarized secretion and formation of the neurons in the first place - see more detailed discussion of this hypothesis in84. This scenario is compatible and can explain multiple origins of neurons (Fig. 6B). Thus, neurons themselves can be functionally defined as polarized secretory cells specialized for neuroplasticity 6, 84. A priori, from the evolutionary standpoint, the term ‘neuron’ should be viewed as a functional category rather than a genetic character. In fact, considering the diversity of life adaptations, the terms of functional category can be applied to the majority of many other fundamental zoological traits such as muscles, mesoderm, anus, mouth, sensory organs. Furthermore, the most of these traits are independently evolved in ctenophores compared to the rest of the animals (Fig. 3). The hypothesis of independent origins of neurons also implies independent origins of electrical and chemical synapses as was recently discussed elsewhere86. It is likely that the origins and evolution of synapse recruited some receptive, secretory and adhesive molecules associated with ancestral stress/injury/immunity responses (Fig. 6C). The evolutionary and molecular classification of synapses would be an important direction for future studies and requires novel microanalytical approaches110, 111. Various forms of learning and memories, elementary and more complex cognition and intelligence are also passive products of biological evolution. Deep ancestry of the neurogenic effects of injury might also explain some puzzling similarities between learning/memory mechanisms and adaptive neuronal injury responses. The hypothesis of Memory of Injury points



out that nociceptive and injury associated molecular/genomic regulatory pathways can be ancestral for both non-associative and associative forms of long-term memory across species including vertebrates112. Among 34-35 phyla, only two lineages of obligate and highly specialized parasites (Myxozoa, as derived Cnidaria, and enigmatic Dicyemida) secondarily loss neurons (Fig. 7). Most parasitic groups, microscopic species or sessile animals did not “lose” neural systems, although they might have a smaller number of neurons compared to their free-living relatives or independently lost some neuronal phenotypes or cell types.

Independent Origins and Convergent Evolution of Centralized Neural Systems The occurrence of 9-12 independent events of nervous system centralizations (i.e., formations of composite brains) from the common bilaterian/cnidarian ancestor with diffuse-like neural systems had been proposed84, 113. About 550-540 million years ago, around the Cambrian explosion (known to be associated to the formations of today’s body-plans114), diffuse-type nerve nets in the urbilaterian began to diversify and independently concentrated, within different bilaterian lineages, in the form of one or more nerve cords or fusion into one or many ganglia. Under this scenario, the centralization and encephalization of neural elements occurred in parallel within each basal bilaterian lineages including (Xenacoelomorpha), several Lophotrochozoa/Spiralia clades, Ecdysozoa, and across Deuterostomia (Fig. 7). Our earlier reconstruction of the convergent evolution of nervous system centralizations in bilaterians84, 113 has recently received an additional confirmation by the comparative analysis of selected dorsoventral patterning genes. Specifically, none of the currently studied species of


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Xenacoelomorpha, Rotifera, Nemertea, Brachiopoda, and Annelida show a conserved molecular regionalization of their nerve cords115, further reinforcing the hypothesis that even similar molecules and genes were recruited independently in different animal lineage for superficially comparable neuroanatomical features. Xenoturbella – the mysterious free-living worm (Fig. 4) - with its diffuse neural system in the skin might represent the ancestral prototype of all bilaterian neural systems116-119. Despite numerous controversies in the past120-122, the clade Xenacoelomorpha (which includes Xenoturbella,

parasitic Nemartodermatida, [e.g., Meara – see Fig 4, and Acoela] is now

recognized as the sister group to the rest of Bilateria123-125. With several species recently discovered125, 126 – Xenoturbella is one of the most critical reference lineages to reconstruct early evolution of nervous systems in Bilateria. Although anatomically, the nervous system in Xenoturbella looks simpler than nerve nets in ctenophores and cnidarians, neurochemically it is comparable or can even be more complex than in other bilaterians127. Xenoturbella possesses many conservative and dozens of novel neuropeptides, as well as other secretory signaling molecules and receptors. In contrast, acoels and related species might lose some ancestral signal molecules and genes and develop a broad array of unique traits. The independent nervous system centralization events also occur within Acoela lineage as compared to other Xenacoelomorpha116, 119, 128-132

.

Two other remarkable examples of the convergent evolution are (a) 4-5 independent events of the neural centralization within the single phylum Mollusca84,

133

, and (b) the

development of memory and visual circuits in Cephalopods46 with numerous molecular, cellular and system innovations including elementary cognition features5, 41, 134. Similar to vertebrates,



coleoid cephalopods independently evolved the closed vasculature system with three hearts supporting their bioenergetic and complex central nervous system organizations46. The genome sequencing revealed that Octopus protocadherin expansion, genes involved in cell-/neuron-specific recognition, shares features with the mammalian protocadherins, including enrichment in neural tissues, clustered head-to-tail orientations in the genome135, 136. A noticeable cephalopod novelty includes a novel Octopus cadherin with 77 extracellular cadherin domains, which has elevated expression in the suckers136. Deep sequencing of transcriptomes of pygmy squids (Idiosepius paradoxus) and chambered nautiluses (Nautilus pompilius) identified three types of genomic innovations in the evolution of complex brains46: (1) recruitment of novel genes into morphogenetic pathways, (2) recombination of various coding and regulatory regions of different genes, often called "evolutionary tinkering" or "co-option", and (3) duplication and divergence of genes. Apparently, in each specific cephalopod lineage, we demonstrated the extensive parallel evolution of various gene families including the type-2 co-option of transcription factors that play important roles in the evolution of the lens and visual neurons. Most evident massive recruitment of novel genes occurred in the evolution of the "camera" eye from Nautilus' "pinhole" eye46. The convergent morphological evolution of the neural systems in cephalopods, as probably in all other bilaterian lineages, have been driven by a mosaic of all types of gene recruitment event, and probably in a cell-type lineage-specific manner.

Molecular diversity of neurons does not support their functional specification


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scRNA-seq with unsupervised clustering reveals that neurons are transcriptionally quite diverse, and this diversity does not directly correlate with their morphology and functions in neural circuits, further emphasizing the need to select structures and species for any comparisons carefully. With such a surprising outcome from single-cell transcriptomics, the old question arises again: What are the broadest categories of neurons and why? And how functionally different neuronal classes can be classified? Rene Descartes introduced the reflex concept, and this influential paradigm led to the view, held until the beginning of the 20th century, that neurons should be divided into two groups: sensory and motor cells. Charles S. Sherrington, Santiago Ramón y Cajal, and other neuroscience pioneers, who started deciphering neural circuits in the 1900s, expanded the neuronal classification to the third cell group - interneurons. But today, interneurons are relatively arbitrarily subdivided into dozens and even hundreds of subclasses based upon species or a structure to be investigated, with the emphasis of such classification criteria as functions, morphology and sometimes the presence or absence of specific markers, neuropeptides, receptors and ion channels. There is vast and highly controversial literature about different classifications of neurons with thousands of papers and hundreds of monographs (see examples of such summaries in20, 137, 138), but there is very little overlap across species27. Surprisingly, the majority of novel molecular approaches including transcriptome analyses139-152 reveal that morphologically similar neuronal classes or neurons of similar transmitter specificity are very different in their molecular makeups153, 154 including more recent data including Drop-seq and 10x Genomic approaches18, 21, 25, 26, 73-76, 81, 82, 92, 94, 155-158. Moreover, neurons cannot be reliably classified into the same taxonomical categories as suggested by the traditional tripartite schemes consisting of sensory, motor and interneurons24, 25, 27, 154, 159. There



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are also little correlations between neuronal morphologies, position in circuits and initial molecular data25, 27, 160-162. As outlined earlier, the apparent paradox and complexity of genetic neuronal programs can be resolved, if we will incorporate developmental and evolutionary trajectories and constraints into neuronal classification as well as trace neuronal origins to distinct ancestral cellular lineages6, 24, 84, 85, 113, 157, 158, 163-167.

Deciphering the evolution of neuronal cell lineages: The conceptual framework and outline of the first steps toward Periodic System of Neurons (see also Box 3). The genealogy of neurons is defined as the reconstruction of sister-type relationships across neural lineages. This task includes modeling the origins of neurons from the ancestral cellular population(s), and identification of functional or genetic constraints to reconstruct gains or losses within neuronal populations. Equally important is the development of criteria to distinguish ‘cell type’ vs. functional ‘cell state’ (Boxes 1

& 2) including changes in

neurogenesis, synaptogenesis, activity, learning, and memory, aging and neuronal death. Such conceptual frameworks view neurons within respective neuronal types as units of evolution.

Cell type as an evolutionary unit As a theoretical model for NeuroSystematics, cell type or cell lineages can be considered as distinct evolutionary units, with preservation of conservative gene regulatory networks16 over time, even hundreds of million years. This definition implies that cell types can ‘evolve’ independently by preserving their own identity by either using the same cohort of transcription factors or other molecular regulatory complexes. For example, such reconstruction was proposed


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for vertebrate ciliary photoreceptors168, and for molluscan identified neurons, when their homologs can be identified in species separated by more than 380 million years ago (see below). However, in contrast to the evolution of species, the evolution of a cell type will be constrained by intracellular microenvironment and the states of other cells within the organisms sharing the same genome. Thus, the evolution of cell types will always be intimately linked and, therefore, more mechanistically concerted at the level of gene regulation than the evolution of species in ecosystems. By identifying sister-type relationships, the reconstructed evolution of cell types, at the first approximation, can also be presented in the form of trees like the evolution of species. The natural classification of neurons/cell types should integrate both functional and genetic differences with a possibility to recruit different genes and their regulators within a complex, but little understood 3D genome architecture (Boxes 1 & 2). Finally, the evolutionary cell type classification should establish quantitative values reflecting inherent phylogenetic relationships between different populations of homologous neurons, not only within a single phyletic lineage consisting of cells from several recently diverged species within a given genus, family and order, but also across classes and, eventually, phyla. The rigorous comparative analyses of cell-specific gene expression patterns at the broad taxonomical scale can be used to test different models of evolution and, potentially validate hypotheses about origins and diversification of various neuronal cell types (Figs. 2, 3, 7, 8). The sister-type/monophyly hypothesis views the origin of novel neuronal lineages via the divergences of repeated ancestral characters – those can be evolutionary conserved generegulatory modules, described as Character Identity Networks (ChIN169,

170

), kernels171,

172

or

core networks173. In contrast, the chimerical-/polygenesis hypothesis of the nature of extant



neural systems (Fig. 2) explains the origins of neurons and neuronal type novelties as recruitments, co-options, fusions of novel gene regulatory modules from numerous nonneuronal, and perhaps unrelated, ancestral cell lineages (Fig. 6B) as outlined earlier6, 84, 85, 113, 163, 174

. Genomic, proteomics, metabolomic and functional data from ctenophores support the model

of convergent evolution of neurons and synapses. But, there are limited data about other animal clades18,

157, 158

for rigorous evolutionary analyses using comparable analytical tools and

bioinformatics pipelines.

Computational and phylogenomic frameworks Although probabilistic phylogenetic methods have been widely used to reconstruct evolutionary relationships (genealogies) among species, proteins, and genes, they can also be applied to cell and tissue types175-177. Thus, it would be possible to treat single-neuron transcriptomes as operational taxonomic units (OUTs) placed at the tips of relevant trees. For example, single-neuron transcriptomes can be deconstructed into multiple parts (e.g., presence/ absence of specific genes, transcription factors, non-coding RNAs, or shared networks expression patterns, etc.) designed to code various neuronal features into a data matrix (e.g., synaptic or neurogenic properties/genes). The appropriate tree-/box-type structures derived from Hammer distance matrices are well-established in the field of statistical geometry178-181. Consequently, these tools, together with probabilistic phylogenetic approaches (e.g., parsimony or maximum-likelihood), can be used to construct trees of neuronal subtypes. These trees would be analogous to species trees, allowing to model transcriptome/gene divergence/convergence in cell-type evolution181, 182.


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The predictions of different models (e.g., monophyly = single origin vs. polygenesis of neurons – multiple origins – Figs. 2 and 3) might be tested computationally with relevant scRNA-seq data provided. For example, neurons, or other neuronal characters that evolve independently will be expected to cluster by their homologous characters (e.g., transcription factors' assemblies, pathways, gene regulatory networks, etc.), rather by species. That is because the transcriptome of each character will accumulate expression changes independently (within an ancestral cellular lineage), and clustering will reflect historical signal of homologs within this particular lineage, which will unite the genetically similar populations of neurons (=cellular homologies) across different species. Importantly, it would also be possible to recover network vs. tree topologies in the clustering of neuronal characters generated from unrelated cellular populations181. As in the case of cancer cells181, the treeness tests can be used to support sistertype relationships among some neuronal subclasses if distinct tree topologies are recovered. Indeed, related characters undergoing concerted evolution will cluster by species [or organ, structure or ganglion] because mutations altering shared genetic machinery will result in similar changes in gene expression across the characters – it will be species-/lineage-specific synaptomorphies183. Genealogically related neuronal populations can also be clustered together, but unrelated ancestral cell lineages would form separate clades, possibly with their unique regulators and signal molecules. The rigorous treeness and related statistics177-180, 183 would be essential in analyzing massive comparative single-neuron transcriptome data generated by massive parallel scRNA-seq data. A sister cell-type model of neuronal evolution can be rejected by producing non-tree-like but net-like topologies10,12 in reconstruction and clustering of neuronal characters. In other words, net-like structures among various cell transcriptomes will support either a chimerical



nature of certain neuronal phenotypes (analogous to hybridization of species or convergent evolution) or even their independent origins. In summary, the entire spectrum of neuronal genealogies might be discovered: from unrelated cell lineages to close sister-type evolutionary relationships. It might be possible to search specific cases to computationally reconstruct ancestral proto-neuronal transcriptomes in at least a few cell lineages. Here, distant neuronal/ancestor secretory cell homologies can be recognized as the result of the deployment of the same character identity network. Homologies, or lack of them, can be first deciphered from transcriptomes in closely related species within a given genus (which would be easy); then we can move to the levels of different families, orders, classes and, eventually, phyla. This strategy, combined with deciphering micro- and macroevolutionary processes at the tissue and cell resolution177, can help to reveal the history of specific gene recruitment events, associated with the origin of novel neuronal subtypes, novelties, and behaviors. The reconstruction of such events would undoubtedly lead to testable hypotheses of how brains, novel circuits, and behaviors evolved: from the genome operation at the level of single-cells to the level of organisms in ecosystems.

Terminological Framework In reconstructions of cellular phylogeny and for neuronal classification, in particular, it is important to distinguish terms of ‘homology,’ ‘convergent’ vs. ‘parallel’ evolution. This terminology is a subject of numerous controversies when applied to molecular, cellular and even organismal levels11. Briefly, ‘homology’ refers to sister relationships arisen from the common ancestry (see also169,

184

). Convergent evolution refers to different genetic and molecular

mechanisms, which independently evolved in both close and distantly related species. Parallel


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evolution refers to a lineage-specific diversification of similar ancestral mechanisms and genes including recruiting the same molecular components for different functions. There are not clear distinctions between convergent and parallel evolution, especially in closely related species11; it is recognized that both terms refer to independently evolved processes. For example, the same cellular phenotypes might evolve within the same species by changing the expression of different genes. On the other hand, similar neuronal or transmitter phenotypes might independently evolve in distantly related species by changing the activity of the same set of genes or even the same ortholog. For simplicity, all cases of independent evolution of given cellular phenotypes can be referred to convergent evolution. In the context of neuronal classification, I think the terminology will be adjusted as novel comparative scRNA-seq data become available.

Taxonomical Framework Recognizing and establishing specific neuronal homologies across classes and phyla is the most challenging task in biology. At the best of my knowledge, there is not a single case, when a robust neuron cell type homology was reconstructed between phyla. For example, we do not know how specific classes of neurons (or their diversity) in flatworms are genealogically related to mollusks (either Aplysia or Octopus) or flies, sea urchins or humans or Xenoturbella. In simplified terms, such Systema Naturae of Neurons, with the census of thousands, and possibly millions of neuronal subtypes, would be analogous to the periodic system of chemical elements with predictive power (Box 3). At the very minimum, the census of major cell/neuronal types should be performed using reference species from each animal phylum (Fig. 8).



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Considering that many reference species have significantly smaller numbers of cells in their neural systems, the existing scRNA-seq technologies for massive parallel sequencing make this task realistically achievable in the nearest future. Moreover, it is even now possible to perform scRNA-seq of virtually all cells within an organisms (or a given embryo or larva) from a broad array of reference species across phyla including ctenophores, sponges, cnidarians, placozoans, many lophotrochozoans, and ecdysozoans, as well as hundreds of larval forms from most animal classes. The key would be the usage of comparable methods and computational tools (which are currently not yet standardized or established). Several animal lineages are ideal to determine short- and long-distant genealogical relationships across neuronal populations. One of the most promising groups is Euthyneura – the lineage of gastropod mollusks, which include opisthobranchs (such as Aplysia, Clione, Tritonia, Pleurobranchaea) and pulmonate species (e.g., Lymnaea and Biomphalaria). The major advantage here is easy access to numerically ‘simpler’ neural systems (5,000-20,000 neurons) and

cells

amenable

for

direct

genomic,

microchemical

and,

most

importantly,

electrophysiological profiling in the context of their stereotyped and learned behaviors. Equally important is the fact that the criteria for neuronal homologies at the level of single functionally identifiable molluscan neurons have been developed and validated since the 1970s13, 14. One of the most famous neuronal types is the MCCs (metacerebral cells), which are a pair of giant serotonin-containing interneurons involved in feeding arousal185-187. The homologs of MCC can be recognized across all Euthyneura: from Lymnaea, Clione and Aplysia to Pleurobranchaea and Tritonia13, 185, 188-190 – it is the level of molluscan subclasses separated by more than 380 million years of evolution in each direction. It is probably the most distant homological lineage of single neurons identified to date.


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The classical A. Remane’s criteria (‘Positional,’ ‘Structural,’ Transitional’), that, when put together, indicate a probabilistic argument for homology, can be further adapted to the cellular homologies based on the similarities in gene expression profiles19, 191-195, and underlying regulatory mechanisms in the genome. The most challenging step would be a quest for distant homologies at the level of large taxonomical categories including orders and, eventually, at the level of different animal classes and phyla. Currently, apart from photoreceptors, there is not a single example of such long-range cell-specific homologies16,

168

. The amazing diversity of

cnidarians, annelids, arthropods, and chordates can also offer numerous reference species for step-by-step analyses of specific neurons within the same genus, family, order and beyond. At this moment, a desired genealogical neuronal classification does not exist, mostly because of the lack of comparative neuron-specific expression data within any phylum and across phyla. A critical mass of about 10-30 million of single cell/single neuron transcriptomes can be collected from representatives of at least 15-20 phyla. Currently, we have limited predictive power, and it is impossible to define any single ancestral pro-neural cell population without at least a few hundred thousand single-cell neuron-type specific data per reference species. Single-cell high-throughput genomics already allows us to capture virtually all neurons from Aplysia and Drosophila (Moroz et al. in preparation) – providing a completely unbiased view of the genomic organization of the brain linked to two critical reference lineages representing Lophotrochozoa/Spiralia and Ecdysozoa. Such analysis has already been completed for all cells in C. elegans18, and other species across Metazoa will follow soon. Arguably, marine animals are most suitable for the proposed goals. A large-scale planetary biodiversity survey must be eventually conducted with the tools of single-cell genomics and transcriptomics



(Fig. 9), because the world ocean contains the greatest taxonomic and body-plan diversity, including the unprecedented variety of neuronal types and adaptations. In the not so distant timeframe, it would be possible to establish a solid foundation for natural genealogical classification of cell/neuronal classes across Metazoa reflecting more than one billion years of divergent evolution. Perhaps, there might be a type of the Periodic System of Neurons, which can be an analog to the Periodic System of Chemical Elements, with predictive power for neuronal phenotypes for the synthetic neuroscience of the future.

Acknowledgments: This work was supported by the United States National Aeronautics and Space Administration (grant NASA-NNX13AJ31G), the National Science Foundation (grants 1146575, 1557923, 1548121 and 1645219) and National Institute of Health (grants R01GM097502, R01MH097062-01A1). The author thanks, Drs. Detlev Arendt, Jacob Musser, Ben Cocanougher and Andrea Kohn for fruitful discussions and shared preliminary data. I thank Emily Dabe and Dr. Andrea Kohn for their comments on the manuscript. I also thank two anonymous reviewers for critical comments and suggestions, especially to the reviewer #1, who stresses the importance of systemic constraints in neuronal cell type evolution and needs for the more rigorous definition of cell types as well as predictability within the concepts of the periodic table of neurons. The ideas outlined in this paper were reported at the Society for Integrative and Comparative Biology symposiums and workshops in 2015-2018.


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Box 1. Neuronal Cell Types and their diversity. Neuronal Cell Type is a set of characteristics, which defines distinct morphological, physiological, secretory, synaptic and plasticity features of a given neuronal cell within circuits and nervous systems. For example, these characteristics include: the maintenance of a unique size, shape and degree of polarity and wiring within specific neuronal populations, their dendritic and axonal patterns, transmitters and receptors, adhesion molecules, energetics; plus neurogenesis related signal transduction pathways, ability to “learn and remember” or resistance to degeneration, etc. The neuronal phenotype of a mature neuron can also be called as the “terminal identity,” and any transcription factors that directly bind to the upstream regions of terminal identity genes are “terminal regulators” 196, 197. The majority of these traits are encoded in the neuronal genome, possibly via a set of transcription factors, non-coding RNAs and other components forming a core of evolutionary conserved generegulatory networks 169, 170, 171, 172, 173. The recruitments of these regulatory modules, repressors or enhancers can be further tuned during development and as results of experience-dependent plasticity. The definition of neuronal type should also include the maintenance or conservation of distinct neuronal phenotypes across many generations – i.e., the ability to evolve relatively independently within the constraint of given organisms. The recognition of homologous neurons across species separated by more than 200-300 million years (see the main text) suggest: (i) that at least some neuronal types are well-conserved in evolution and have unique markers for their identities, and (ii) there are molecular and system constraints, which contribute to both preservation of ancestral cell types (possible due to the presence of core gene regulatory networks or modules) and evolution of neuronal novelties and novel neuronal types. Single-cell genomics with quantification of expressed protein-coding and non-coding RNAs as well as unbiased molecular profiling (proteomics, lipidomics78 and metabolomics79) together with mathematical and computational algorithms73, 76, 155, 156, 198-208 can provide the initial experimental strategies to identify core regulatory machinery and effectors responsible for the maintenance and preservation of distinct neuronal phenotypes. In contrast to the evolution of species, the evolution of a cell type will be constrained by intracellular microenvironment and the states of other cells within the organisms sharing the same genome. Thus, the evolution of cell types will always be intimately linked and, therefore, more mechanistically concerted at the level of gene regulation than the evolution of species in ecosystems.



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Box 2. Neuronal State(s): One cell type – many cell states. Neuronal States. Neurons of the same types change their phenotypes and gene expression patterns during development, aging, stress, in a variety of disease stages as well as during learning and memory. Moreover, it was confirmed that some neurons and other cell types could switch (neuro)transmitters209, effectively taking on different secretory identities and potentially can be classified as different cell types. Besides, every neuron may express genes in a stochastic manner at any cell state or during the transition to different states, and even the level of “stochasticity” can be a neuronal type-specific characteristic. Thus, the overall molecular blueprint and classification of neurons at different functional stages might be distinct, from initially recognized neuronal types at any given snapshot. With sufficient statistical data over time (the time-course can be minutes, to days or months), it might be possible to recognize and visualize different states within the concept of the Waddington landscape88, 210, when neurons might “occupy” different hills and valleys – creating a broad spectrum of phenotypic space, which is defined by yet unknown internal and systemic constraints. In formal mathematical terms, it can be multidimensional space (thousands of dimensions!). At this moment, the entire field of unbiased molecular classification of neurons is in its infancy. Moreover, currently, none of the known individual identified neurons have been profiled sufficiently molecularly at different stages and compared to the entire neuronal population in the brain of an organism. Therefore, we do not yet know how to predict a neuronal type vs. neuronal state(s) or to estimate their overlaps across a diversity of brains without primary experimental evidence and quantification of stochastic events in a cell-specific manner. Thus, the ongoing single-cell genomic revolution will provide critical data to determine the probabilities of the existence of intermediate cell types or transient cell states. Initial scRNA-seq data from stem cells, during development and differentiation, provide an initial mathematical framework, statistical algorithms with machine learning tools to visualize and distinguish different cell trajectories, and eventually cell states25, 198208, 211-214 . By analogy to micro and macroevolutionary scales in the origin of species, one might envision a comparable spectrum of discrete and intermediate units (a series of phenotypic spaces) in the evolution of cell types; potentially with less discrete but still recognized transitions between cell classes and states. Nevertheless, rather than a continuum of all possible transitions, discrete ‘islands’ of genomic stability corresponding to distinct neuronal (pheno)types can be revealed by deep single-cell molecular/genomic analyses and by the unbiased census of all cells forming the entire neural systems across representative species.


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Box 3. Periodic System of Neurons. “Periodic System of Neurons” can be considered as a broad conceptual framework, analogous to the Periodic System of Chemical Elements, with a predictive power to recognize either preservation of ancestral cell lineages or formation of new neuronal cell types in development and evolution. At the first approximation, it can be referred to as a “simple predictive system” for the reconstruction and classification of specific neuronal lineages and respective core gene regulatory networks, signaling molecules, neuronal markers or functions. The presence of apparently independent diversification of neuronal cell types within of a given phylum and the emerging evidence for convergent evolution6, 86, together with physical, chemical, bioenergetic constraints at all levels of molecular and system organization, might imply the reuse and recruitments of similar principles in different animal lineages. Building on the analogy to the Periodic System of Elements, our model suggests some degrees of periodicity (e.g., in recruitments of functionally similar genes, domains, molecular clusters, etc.), defined by the pre-existing constraints and shared molecular modules, within the majority of 30+ phyla, or more than 100 classes of animals. In such cases, constraints within phyla, classes or even lower level of taxonomical units (or recognized ancestral cell lineages) might be analogous to ‘periods’ in the chemistry of elements. Of course, the applicability of such a concept (or alternative models at the scale of the entire animal kingdom) can be only tested experimentally using a diversity of reference species including those outlined in the manuscript. I would not be surprised if more than one “Periodic System” (or even more complex classification) of cell types across Metazoa would emerge in the future.



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Figure Legends:

Figure 1. Selected reference species for massive parallel single-cell sequencing of the entire neural systems (Brain-seq). Although the complexity of human and other mammalian brains prevents a complete census of cells in these species, such analysis and atlas of ALL cells and cell types is fully achievable today for numerically simpler neural systems of nematodes, flies and sea slugs.

Figure 2. Polygenesis of Neurons from Ancestral Cell Lineages: Genealogy of neurons is the reconstruction of their evolutionary history from ancestral, probably distantly related, cell lineages (different color trajectories). Neural systems might consist of genetically highly diverged cell types with different ancestries, gene regulatory networks, and signal molecules. This diagram integrates both the hypothesis of independent origins of neurons (as in ctenophores6, 85, 163) and the sister-cell model175, which suggests that novel neuronal types arise in pairs, through sub-specialization of ancestral cell types. Thus, sister neuronal sub-types can share gene-regulatory networks, perhaps, evolutionary conserved developmental pathways, and are expected to have more similar expression profiles than each of them compared to other neuronal types. The key prediction of this model is that gene-expression profiles from sister-cell types will form a hierarchical tree structure in phylogenetic reconstructions176,

178

. An

alternative model (=independent origins) predicts that neurons and novel neuronal sub-types arise through ‘co-options’ or ‘fusions’ of regulatory modules and pathways ‘recruited’ from genetically unrelated cell types178,

183

. As a result, their expression profiles would be substantially different, leading to net-type

rather than tree-type cellular genealogies in phylogenetic reconstructions. We expect that both scenarios can co-exist in any given nervous system. But the tremendous diversity of neural systems across phyla (Figs. 1, 3, 4, 7) suggests variable contributions of each historical scenario. Combining tools of (i) statistical geometry179, 180, 215 and (ii) modern phylogenomics85, 99, 120, 122, 133, 216 with (iii) massive parallel single-neuron transcriptome profiling would allow us to unbiasedly reconstruct the genealogy of neurons by testing the treeness statistics as it was recently used for cancer and placental cells176, 178.


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Figure 3. Novel animal phylogeny suggests the extensive convergent evolution of many traits in Metazoa including independent origins of neurons, synapses, muscles, and mesoderm.

The

reconstruction is based on recent genomic85, 86 and phylogenomic85, 99, 100 data (see Fig. 7 for the bilaterian phylogeny). Do ctenophores have the same neuronal molecular machinery as other Eumetazoans? No: Neurogenic and signaling molecules in this lineage are quite different (both molecularly and in their expression patterns) supporting convergent evolution on neural systems. Modified from85, 86.

Figure 4. Examples of emerging reference species for evolutionary neuroscience. These species represent lineages of animals critical for the analysis of neuronal origins and parallel evolution of neuronal centralizations. None of these species can be maintained in the land laboratory, and their studies require fieldwork. The term “reference species” is preferable vs. “model organisms” (for details see text and87). Recent comparative data indicate that there are no pan-neuronal/pan-synaptic genes across Metazoa104. The phylum Ctenophora is represented by the cydippid Pleurobrachia bachei, the lobates Beroe ovata (Florida) and Mnemiopsis leidyi (Woods Hole, Atlantic); Cnidaria – the scyphozoan Aurelia aurita (Atlantic) and a brain coral; basal bilaterians - Xenoturbellida – Xenoturbella bocki (Northern Sea); Nemertodermatida – Meara stichopi (Northern Atlantic); Mollusca – the cephalopod Sepia officinalis and Clione antarctica (Weddell Sea); Arthropoda – Limulus polyphemus, trilobite larva; Annelida – unidentified tube worm (Palau); Priapulida – Priapulus caudatus (Northern Atlantic); Echinodermata – unidentified crinoid from the Great Barrier Reef (Heron Island, Australia); Hemichordata – tornaria larva (Bahamas). The author obtained the photographs during his field expeditions.

Figure 5. The absence of serotonin in ctenophores and its detection in hemichordates (modified from 85

Here we used nanolitre volume sampling, capillary electrophoresis separation, and wavelength-resolved

native fluorescence detection as described for ultra-sensitive assay of 5-hydroxytryptamine (serotonin or 5-HT) and related metabolites217 (the top electropherogram with standards used). Limits of detection



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(LODs) range from the low attomole to the femtomole range, with 5-HT LODs being approximately 20– 50 attomoles. Using this assay, we failed to detect 5-HT in Pleurobrachia, but 5-HT was reliably detected in the hemichordate Saccoglossus (see the respective animal photos on the top left).

Figure 6. Hypotheses of the origins of neurons and synapses. A. Stages of the neuronal specification from conductive myoepithelial cells – the monophyletic (single-origin) scenario107, 108. B. Multiple origins of neurons from secretory cells- the polygenesis scenario17, 84, 218. Here, neurons are defined as polarized secretory cells specialized for directional active conducting – the features that enable them to transmit signals, primarily chemical, beyond their immediate neighbors without affecting all intervening cells en route, and with learning & memory capabilities6. Neurons can evolve from several types of secretory cells. The coronary of independent origins of neurons is independent origins of synapses86. C. Injury as a neurogenic signal in evolution84, 112 leading to a co-release of signal molecules. Different types of synaptic signaling and rearrangement emphasize distinct forms and mechanisms of learning.

Figure 7. Convergent evolution of bilaterian centralization and brains. The modern view of relationships among animal phyla reconstructed using phylogenomic approaches219-221,

85, 99, 133

. Many

nodes (e.g., within Ecdysozoa and Lophotrochozoa) are not yet resolved. # - The clade Xenoacoelomorpha120, 122, 222, which is placed at the base of Bilateria123-125. This tree is combined with the presence or absence of a central nervous system or brain. Examples of parallel centralization of neural systems and secondarily loss of nervous systems in obligatory parasites are shown. Insert shows abbreviations: ‘smiling faces’ – brains or a brain-type of the neural organization including ganglionic nervous systems. It appears than neuronal centralization evolved multiple times independently over >600 million years of biological evolution. More than four centralization events can be identified in mollusks. ‘’Clouds” illustrate primarily diffuse nervous systems, neural nets, and ancestral conditions; “Stop sign” – illustrates two lineages (Myxozoa and Dicyemida223, red text) with a primary absence of neural


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organization. Orthonectida was originally considered as a nerveless group. However, in the orthonectid Intoshia linei, with the recently sequenced genome224, neurons have been identified225. Porifera and Placozoa do not have recognized neurons, and Urmetazoan – the hypothetical ancestor of all living Metazoans did not possess neurons or muscles either. Thus, the absence of neurons and muscle is a primary condition before the diversification of basal metazoan lineages. Ctenophores evolved neurons, synapses and, possibly, muscles as well as mesoderm, independently from other groups of animals86. Although neuronal organization in Cnidaria and Ctenophora can be superficially presented as a nerve net or diffuse nervous system, many species have a prominent concentration of neuronal elements, and numerous autonomous networks are governing surprisingly complex and well-coordinated behaviors. For example, there are well-defined concentrations of neural elements associated with locomotory combs, the aboral organ in Ctenophora, and rhopalia in Cnidaria, especially prominent in Cubozoa. Choanoflagellates are placed at the base of the tree as a sister group for Metazoa.

Figure 8. Metazoan cell/neuronal lineages at the single-cell resolution. Conceptional overview. These are illustrative examples of massive parallel single-cell RNA-seq from several reference species representing all basal metazoan lineages obtained by the author laboratory (photos by L.L. Moroz). Inserts show tSNE (T-distributed Stochastic Neighbor Embedding) clustering of individual cells from the following respective species: the ctenophore - Pleurobrachia bachei [more than 50 distinct cell types have been identified in the adult]; the Xenacoelomorpha - Xenoturbella bocki [>30 cell types identified]; the ecdysozoan - the fruit fly Drosophila melanogaster [>150 cell types]; the lophotrochozoan - the sea slug Aplysia californica [>160 cell types]; the deuterostome - the tornaria larva of hemichordate Ptychodera flava [>40 cell types] (L.L. Moroz, B. Cocanougher, B. Swalla, P. Martinez and A.B. Kohn unpublished data).



Figure 9. Deciphering of the genealogy of metazoan cell lineages by single-cell analysis of Planetary Biodiversity. Conceptional overview. See text and15 for details. Earth Photo: NASA (https://eoimages.gsfc.nasa.gov/images/imagerecords/46000/46209/earth_pacific_lrg.jpg)


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Figure 1. Selected reference species for massive parallel single-cell sequencing of the entire neural systems (Brain-seq). Although the complexity of human and other mammalian brains prevents a complete census of cells in these species, such analysis and atlas of ALL cell and cell types is fully achievable today for numerically simpler neural systems of nematodes, flies and sea slugs. 464x320mm (96 x 96 DPI)



Figure 2. Polygenesis of Neurons from Ancestral Cell Lineages: Genealogy of neurons is the genealogy of ancestral, probably unrelated or distantly related, cell lineages (different color trajectories). 330x288mm (96 x 96 DPI)


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Figure 3. Novel animal phylogeny suggests the extensive convergent evolution of many traits in Metazoa including independent origins of neurons, synapses, muscles, and mesoderm. 405x249mm (96 x 96 DPI)



Figure 4. Examples of emerging reference species for evolutionary neuroscience. 404x246mm (96 x 96 DPI)


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Figure 5. The absence of serotonin in ctenophores and its detected in hemichordates

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Figure 6. Hypotheses of the origins of neurons and synapses.

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Figure 7. Convergent evolution of bilaterian centralization and brains. 297x303mm (96 x 96 DPI)



Figure 8. Metazoan cell/neuronal lineages at the single-cell resolution. Conceptional overview. These are illustrative examples of massive parallel single-cell RNA-seq from several reference species representing all basal metazoan lineages obtained by the author laboratory (photos by L.L. Moroz). Inserts show tSNE (T-distributed Stochastic Neighbor Embedding) clustering of individual cells from the following respective species: the ctenophore - Pleurobrachia bachei [more than 50 distinct cell types have been identified in the adult]; the Xenacoelomorpha - Xenoturbella bocki [>30 cell types identified]; the ecdysozoan - the fruit fly Drosophila melanogaster [>150 cell types]; the lophotrochozoan - the sea slug Aplysia californica [>160 cell types]; the deuterostome - the tornaria larva of hemichordate Ptychodera flava [>40 cell types] (L.L. Moroz, B. Cocanougher, B. Swalla, P. Martinez and A.B. Kohn unpublished data). 365x301mm (96 x 96 DPI)


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Figure 9. Deciphering of the genealogy of metazoan cell lineages by single-cell analysis of Planetary Biodiversity. Conceptional overview. 442x320mm (96 x 96 DPI)



For Table of Contents Use Only 317x202mm (96 x 96 DPI)


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NeuroSystematics and Periodic System of Neurons:

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