Molecular Architecture of the Octopus bimaculoides Central Nervous System by Jeremea Ocampo Songco A dissertation accepted and approved in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biology Dissertation Committee: Dr. Chris Q. Doe, Chair Dr. Cristopher M. Niell, Advisor Dr. Adam C. Miller, Core Member Dr. Emily L. Sylwestrak, Core Member Dr. Matthew C. Smear, Institutional Representative University of Oregon Summer 2024 2 © 2024 Jeremea Ocampo Songco 3 DISSERTATION ABSTRACT Jeremea Ocampo Songco Doctor of Philosophy in Biology Title: Molecular Architecture of the Octopus bimaculoides Central Nervous System Interacting with our environments requires that we appropriately integrate sensory information and convert these inputs into a perception of our surroundings to generate basic and complex behaviors. Traditionally, model organisms, such as nematodes, flies, zebrafish, or even mice, have been used in the laboratory setting to investigate neural circuit formation and function. While these organisms have furthered our understanding of how different cell types wire up to drive complex behavior, there is much to be learned from exploring the brain of non- traditional organisms. Cephalopods have the largest brain among invertebrates and have a rich catalog of behaviors, including navigating complex underwater environments and rapid body-patterning known as camouflage. While seminal work during the 1960s revealed cellular properties of neurons using the giant squid axon, recent advancements in technology have permitted further characterization of cell types and circuits in a species that is unlike many of those used traditionally in the field of neuroscience. By investigating the brain of these animals, we can begin to understand fundamental mechanisms involved in the formation and function of complex neural circuits. Unlike model organisms, there are limited tools in genetic manipulation and the field has yet to produce a comprehensive brain atlas bridging anatomical, molecular, and functional properties of cell types in these animals. Therefore, my dissertation sought to develop key resources that will serve as a foundation for such studies once it becomes technically possible. I first contributed to the optimization and usage of functional imaging in an ex vivo preparation of the octopus brain in order to characterize response properties of visually responsive cells in the optic lobe, the main visual center which is a paired brain region that comprises 2/3 of the central nervous system of octopuses. We found evidence for retinotopic organization of responses to light (ON) and dark (OFF) spots, including spatial tuning properties that may be suggestive of 4 environmental demands. To begin elucidating the diversity of unit responses we revealed in this initial study, I focused on developing a single-cell molecular atlas of the Octopus bimaculoides optic lobe by combining single cell RNA-sequencing (scRNA-seq) with multiplexed fluorescence in situ hybridization (FISH). We identified six classes of mature neuronal cell types in addition to a large population of immature neurons. Our FISH revealed sublaminar organization across the optic lobe, further characterizing the cell types that were initially identified in the 1960s based on morphology. An octopus’ ability to engage in a wide range of visually guided behaviors rests upon the various inputs and outputs the optic lobes have to other structures in the central nervous system. However, there has yet to be published a mapping of these structures as well as an in- depth understanding of the molecular landscape across the central nervous system. Therefore, I sought to develop the first brain-wide gene expression resource for cephalopods by characterizing all of the structures in this species through Hematoxylin & Eoisin (H&E) staining of serial sections of the brain, and I quantified expression for 40 genes, including functional and developmental determinants, across 20 identified brain regions. Together, this work reveals functional and molecular organization in the optic lobe as well as other brain regions, furthering our understanding of how a completely different organism can carry out complex behaviors. This dissertation includes previously published and unpublished co-authored material. 5 CURRICULUM VITAE NAME OF AUTHOR: Jeremea Ocampo Songco-Casey GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED: University of Oregon, Eugene San Diego State University, San Diego, California DEGREES AWARDED: Doctor of Philosophy, Biology, 2024 University of Oregon Master of Science, Biology, 2021 University of Oregon Bachelor of Arts, Psychology, 2018 San Diego State University AREAS OF SPECIAL INTEREST: Neuroscience Molecular Biology Developmental Neurobiology PROFESSIONAL EXPERIENCE: Graduate Researcher, University of Oregon, 2018-24 Laboratory of Dr. Cristopher Niell Graduate Teaching Assistant, University of Oregon, 2018-20 Undergraduate Research Assistant, San Diego State University, 2015-18 Laboratory of Dr. Claire Murphy Summer Undergraduate Research Assistant, University of Washington, 2017 Laboratory of Dr. David Gire GRANTS, AWARDS, AND HONORS: NIH BRAIN Initiative Grant Diversity Supplement, University of Oregon, 2021-23 National Institutes of Health, Brain Research Through Advancing Innovative Neurotechnologies Initiative 6 Science Coalition’s “Fund It Forward” Student Video Challenge Winner, 2022 Department of Biology Diversity, Equity, & Inclusion Grant, submitted on behalf of Womxn in Neuroscience, Anti-Racism Training, University of Oregon, 2021 ARCS Scholar Award, University of Oregon, 2018-21 Achievement Rewards for College Scientists, Oregon Chapter HHMI Gilliam Fellowship Nomination, University of Oregon, 2020 Howard Hughes Medical Institute Graduate Research Fellowship Program Honorable Mention, University of Oregon, 2020 National Science Foundation WiN Scholarship, University of Oregon, 2019 Womxn in Neuroscience CMiS Travel Award, University of Oregon, 2019 A Community for Minorities in STEM Promising Scholars Award, University of Oregon, 2018 Maximizing Access to Research Careers Scholar, San Diego State University, 2016-18 Funded by the National Institutes of General Medical Science SACNAS Travel Award, San Diego State University, 2016 Society of Advancing Chicanos/Hispanics and Native Americans in Science Cox Cares Scholarship, San Diego State University, 2014-15 Funded by the San Diego Scholarship Foundation Lehman Family Scholarship, San Diego State University, 2014-15 Funded by the San Diego Scholarship Foundation 7 PUBLICATIONS: Coffing, G. C., Tittes, S., Small, S. T., Songco-Casey, J. O., Piscopo, D. M., Pungor, J. R., Miller, A. C., Niell, C. M., & Kern, A. D. (2024). Cephalopod Sex Determination and its Ancient Evolutionary Origin Revealed by Chromosome-level Assembly of the California Two-Spot Octopus. bioRxiv. Pungor, J.R., Allen, V.A., Songco-Casey, J.O., and Niell, C.M. (2023). Functional organization of response properties in the octopus optic lobe. Curr. Biol. 33, 2784-2793.e3. Baden, T., Briseño, J., Coffing, G., Cohen-Bodénès, S., Courtney, A., Dickerson, D., Dölen, G., Fiorito, G., Gestal, C., Gustafson, T., Heath-Heckman, E., Hua, Q., Imperadore, P., Kimbara, R., Król, M., Lajbner, Z., Lichilín, N., Macchi, F., McCoy, M. J., … Songco-Casey, J.O., … Albertin, C. B. (2023). Cephalopod-omics: Emerging Fields and Technologies in Cephalopod Biology. Integrative And Comparative Biology, 63(6), 1226–1239. Songco-Casey, J.O., Coffing, G.C., Piscopo, D.M., Pungor, J.R., Kern, A.D., Miller, A.C., and Niell, C.M. (2022). Cell types and molecular architecture of the Octopus bimaculoides visual system. Curr. Biol. 32, 5031–5044.e4. Findley, T. M., Wyrick, D. G., Cramer, J. L., Brown, M. A., Holcomb, B., Attey, R., Yeh, D., Monasevitch, E., Nouboussi, N., Cullen, I., Songco, J. O., King, J. F., Ahmadian, Y., & Smear, M. C. (2021). Sniff-synchronized, gradient-guided olfactory search by freely moving mice. eLife, 10, e58523. http://paperpile.com/b/WfZi5S/fWXWD http://paperpile.com/b/WfZi5S/fWXWD 8 ACKNOWLEDGMENTS I would like to express sincere appreciation to Dr. Cristopher Niell for his mentorship and guidance on the work presented in this dissertation. His enthusiasm for science and out-of-the- box thinking has been inspiring to witness and learn from. His encouragement to pursue the “weird” questions in science played a large role in cultivating my curiosity about the brain and is a major factor for why cephalopod neuroscience will always hold a special place in my heart. I am equally thankful to Dr. Adam Miller. Our 1:1 meetings were truly impactful, especially earlier on in my career, as he helped me understand what I know and what I don’t know in the fields of molecular and developmental biology. He imparted wisdom onto me—both with respect to scientific projects and outside. He is truly dedicated to the growth of trainees in and outside of his lab in a way I hope to emulate in the next stages of my career as well. I extend deep gratitude to my DAC as a whole – Dr. Chris Doe, Dr. Cristopher Niell, Dr. Adam Miller, Dr. Emily Sylwestrak, and Dr. Matt Smear. They challenged me in ways that allowed me to grow as a rigorous and detail-oriented scientist earlier on. They provided a welcoming space for cultivating both personal and professional growth and were supportive in all of my endeavors. To members of the Niell Lab – past and present: I am sincerely grateful for all of the mentorship and support I received from you all, in and outside of lab. Despite entering the ION program as a cohort of 1, all of you were quick to welcome me into the lab and helped me feel like part of the community. To Dr. Denise Niell, without you, I would not have learned as much as I did about molecular biology. Your rigorous approach to research has critically taught me to become a better scientist as well. I especially need to thank Dr. Angie Michaiel, Dr. Elliot Abe and Dr. Philip Parker, who have provided endless support for my research and professional goals and continue to do so to this day. Thank you all for being excellent mentors and friends. I wish to express my sincere appreciate to many of my peers within the graduate program, including Dr. Rachel Lukowicz Bedford, Alyssa Quiogue, and Lucy Moholt-Siebert. In each of you, I have found unwavering support and scientific inspiration. Your approaches to research and mentoring have shown me alternate ways of thinking and inspired creativity in my work as a result. Thank you for accepting me. 9 I am also thankful for the friends I’ve kept and made during the last six years. Despite our varied interests, I am grateful for the friends I have in San Diego. Equally so, I am thankful for the friends I’ve made since starting grad school, especially those who have served as examples for science advocacy. Despite not being at the same institution and having only met briefly, each of you were impactful on my journey to graduating. I would also like to thank peers I’ve met outside of the program – specifically, those that I played soccer alongside as well as those that I’ve had the pleasure to learn from while at Thermo Fisher Scientific for an internship. All of my soccer teammates have provided a much- needed reprieve from science when experiments weren’t going as planned. To the friends I’ve made through my internship: thank you for welcoming me and relying on me as one of your own. I grew so much in my short time at Thermo Fisher Scientific and gained both technical and organizational skills in my experience. To Dr. Scott Clarke and Dr. Leticia Montoya: thank you for teaching me to be a better and more efficient scientist. I am especially grateful to Dr. Alexia Bachir. Your trust in me reminded me of how capable I am, and your mentorship leaves a lasting impact on me. To the MARC program at SDSU: this is where the journey started, and I need to share my deep gratitude for the support I received from all of my peers and mentors in the program. Thank you for supporting my goals then and now. To the ARCS Foundation, thank you for believing in my ability to be a great scientist even before meeting me. I am especially grateful for Caron, Larry, and Lara, whom I have grown close to over the last six years and always feel supported by in my research and professional goals. Lastly, I would like to thank the University of Oregon G3 Core, Imaging Core, including Adam Fries and Leah DeBlander, and histology support provided by Dr. Poh-Keng Loi and Brandon Wiskow, and the Marine Biological Laboratories for our animals. I acknowledge support by the ARCS Foundation and University of Oregon Promising Scholars Award. The investigations outlined in this dissertation were supported in part by the National Institutes of Health Brain Research Through Advancing Innovative Neurotechnologies Initiative R01NS118466-01S1 as well as from the following grants to Dr. Cristopher Niell (NIH R01NS118466-01, Office of Naval Research N00014-21-1-2426) and jointly to Dr. Cristopher Niell and Dr. Adam Miller (University of Oregon Renee James Seed grant). 10 DEDICATION This work is dedicated to my loving family and husband. To my parents, Federico and Eva Songco: thank you for teaching me the value of education when I was still young; for reminding me that an education is a privilege – one that cannot be taken away once you’ve obtained it. The sacrifices you made to come to the United States and give us a better life do not go unnoticed, and I am proud to be your daughter. To my sisters, Ferjea Sahagun and Redjean Songco: thank you for being sisters by blood, best friends by choice. Thank you for your support in this journey and for reminding me of my best qualities during the roughest of times. To my late grandma, Clarita Ocampo. Memories of your selflessness, happiness, and strength inspire me to remain resilient, light, and hopeful. Thank you for guiding me from afar. To my husband, Nathan Michael Casey: I am at this finish line because of you. Thank you for believing in me from the beginning, for making my life easier, and for all of the sacrifices you’ve made to support me in this goal. Your strength, resilience, and wisdom inspire me to be better, every day. Your unconditional love fuels me. Thank you for being a light in my life. I am here because of you. To our boys: I am thankful to have had the opportunity to spend these last few months with each of you. Theo Alexander, you will always be in our hearts. Baby A, thank you for the little taps reminding me I have you keeping me company while writing. We can’t wait to meet you soon. 11 Chapter Page I. CHAPTER I INTRODUCTION ....................................................................................... 17 Octopus Brains as a Model for Understanding Complexity ................................. 17 Goals of Dissertation ............................................................................................. 20 References ........................................................................................................................ 21 II. FUNCTIONAL ORGANIZATION OF VISUAL RESPONSES IN THE OCTOPUS OPTIC LOBE.................................................................................................................... 24 Introduction ..................................................................................................................... 24 Results .............................................................................................................................. 26 Calcium Imaging of Stimulus-Specific Visual Responses in the Optic Lobe ...... 26 Spatially Localized ON and OFF Receptive Fields .............................................. 29 Retinotopic Organization of the Optic Lobe ......................................................... 32 Discussion ........................................................................................................................ 36 Spatial Organization of Response Properties in the Optic Lobe ........................... 36 Comparative Aspects of ON/OFF Pathways and Spatial Processing ................... 37 Implications for Future Studies ............................................................................. 38 Methods ............................................................................................................................ 39 Experimental Model and Subject Details ............................................................. 39 Method Details ...................................................................................................... 40 References ........................................................................................................................ 45 III. CELL TYPES AND MOLECULAR ARCHITECTURE OF THE OCTOPUS BIMACULOIDES VISUAL SYSTEM ............................................................................. 50 Introduction ..................................................................................................................... 50 Results .............................................................................................................................. 52 ScRNA-Seq of the Octopus Optic Lobe ............................................................... 52 A Molecular and Spatial Taxonomy of Mature Neural Cell Types ...................... 56 Immature Neurons ................................................................................................ 62 12 Cell-Type and Sub-Layer Organization of Mature Neurons in the Optic Lobe ... 66 Discussion ........................................................................................................................ 68 Implications for Future Studies ............................................................................. 70 Methods ............................................................................................................................ 71 Experimental Model and Subject Details ............................................................. 71 Method Details ...................................................................................................... 71 Single-Cell cDNA Library Preparation ................................................................ 73 Quantification and Statistical Analysis ................................................................. 75 Cluster Analysis .................................................................................................... 75 Methods S1. Elucidating Unidentified Genes. Related to Figures 4, 5, and 7. .......... 76 obimac0010569 ..................................................................................................... 76 obimac0022194 ..................................................................................................... 77 obimac0019980 ..................................................................................................... 77 Supplementary Figures .................................................................................................. 79 References ........................................................................................................................ 87 IV. BRAIN-WIDE GENE EXPRESSION IN THE JUVENILE OCTOPUS BIMACULOIDES .............................................................................................................. 94 Introduction ..................................................................................................................... 94 Results .............................................................................................................................. 95 Serial Sectioning in the Juvenile Octopus bimaculoides ...................................... 95 Differential Expression of Neurotransmitters and Neuropeptides ........................ 96 Characterization of Developmental Genes ......................................................... 102 Discussion ...................................................................................................................... 109 Limitations of This Study ................................................................................... 113 Methods .......................................................................................................................... 114 Experimental Model and Subject Details ........................................................... 114 Method Details .................................................................................................... 114 13 Supplementary Figures ................................................................................................ 116 References ...................................................................................................................... 120 V. CHAPTER V CONCLUDING REMARKS ................................................................... 128 References ...................................................................................................................... 130 14 LIST OF FIGURES Figure Page CHAPTER I 1. Overview of cephalopod brain anatomy organization ..................................... 18 CHAPTER II 1. Experimental paradigm for calcium imaging of visual responses in the optic lobe ................................................................................................ 25 2. Visually evoked responses in the optic lobe .................................................... 28 3. ON and OFF receptive fields mapped with a sparse noise stimulus ................ 29 4. Retinotopic organization of visual responses in the octopus optic lobe .......... 31 5. Size selectivity and temporal dynamics across the layers of the optic lobe .... 34 CHAPTER III 1. Laminar organization of the O. bimaculoides optic lobes ............................... 50 2. scRNA-seq reveals six major neuronal classes ................................................ 53 3. Neurotransmitter usage divides the majority of cells into four large populations ...................................................................................... 56 4. Anatomical organization of major cell classes and subtypes based on scRNA-seq and FISH ........................................................................ 58 5. Gene expression and spatial organization of putative immature neurons ........ 62 6. Expression of conserved patterning molecules ................................................ 64 7. Summary of mature neuronal architecture ....................................................... 65 8. AlphaFold prediction for obimac0022194 showing the structure of the rank1 model ........................................................................................... 76 9. Evidence of a globular protein plus signal peptide structure for obimac0019980 as predicted by DeepTMHMM ....................................... 77 15 10. Genome browser output showing improved gene models ............................... 79 11. Single-cell RNA sequencing quality control metrics ....................................... 81 12. Characterization of non-neuronal clusters ....................................................... 82 13. Validation of glutamatergic and cholinergic markers ...................................... 84 CHAPTER IV 1. Example of coronal samples ............................................................................ 95 2. Preliminary DotPlot summary quantifying expression levels and cell density of 40 genes across 20 brain regions ....................................... 97 3. Neurotransmitter expression ............................................................................ 99 4. Expression of octopaminergic signaling, transportation, and receptor genes provide insight into potential functionally related regions .............................. 100 5. Differential expression of 5 neuropeptides ...................................................... 101 6. Differential expression of homeobox genes .................................................... 104 7. Differential expression of developmental genes and markers for immature neurons ......................................................................... 106 8. Complementary expression of patterning molecules ....................................... 108 9. Circuit diagram of tactile and visual learning and memory ............................. 111 10. Co-localization of neuropeptides with homeobox genes ................................. 116 11. Featureplots showing single-cell expression of dscam, lrrc, and hh ............... 117 16 LIST OF TABLES Table Page CHAPTER III 1. Genome assembly statistics for o_bimaculoides_hifi_v1.0.0 and Octopus_bimaculoides_v2_0 .................................................................... 85 2. Reference gene table ........................................................................................ 86 CHAPTER IV 1. Comparison of identified lobes in the adult Octopus vulgaris central nervous system versus the juvenile Octopus bimaculoides ................. 118 2. Candidate genes used for RNA FISH .............................................................. 119 17 CHAPTER I INTRODUCTION The ability to interact with our rich environments requires that our brain convert sensory information into a perception of our surroundings. Tasks we likely engage in every day without giving it a single thought utilize rapid computations carried out by intricate neural circuits shaped by our biology and our experiences. To understand how these basic and complex behaviors are carried out, it is critical to understand the components that contribute to their underlying circuitry. Often, investigations into neural circuit formation and function utilize traditional model organisms, which are defined by their short, closed lifespan, ability to reproduce many offspring and generations, and the availability of genetic tools1. Through using model organisms in the laboratory setting, we have learned about critical features of neural circuits – from development to function, behavior, and, even, pathology (Reviewed in2–6). However, investigating different embodiments of advanced neural processing can facilitate our understanding of the fundamentals of how these circuits form and function, therefore elucidating, for simplicity’s sake, “how to build a complex brain”. Due to their large brains, complex nervous systems, and advanced behaviors, cephalopods are an intriguing emerging model system for expanding our understanding of how a completely different brain executes dynamic computations. Octopus Brains as a Model for Understanding Complexity Cephalopods, which includes squid, cuttlefish, and octopuses, have the largest brain among invertebrates7–10, with their central nervous system—as well as each of their arms—serving as a rich network of neuronal communication11. The cephalopod central nervous system is located at the base of the mantle, between the eyes of the animal (Figure 1). It is characterized by two masses, one that sits above the esophagus, termed the supraesophageal mass (SEM), while the other sits right below the esophagus (subesophageal mass; SUB)11. These masses are in between the two large optic lobes, which comprise ⅔ of the entire central nervous system and are considered to be the main visual processing areas12,13. In reference to the body-axis, the SEM is 18 considered dorsal and contains brain areas responsible for mantle control and regulation of visceral organs, whereas the SUB is ventral and comprises brain areas responsible for controlling the arms14–16. Figure 1. Overview of cephalopod brain anatomy organization. A) Location and 3D rendering of major structures in the Octopus brain. B) Sagittal section displaying lobes of the SEM and SUB, from 17. C) Diagram of major structures in the octopus brain, viewed from the front. Modified from 18 (top). Diagram of octopus brain organization seen from above, adapted from 11 (middle). Diagram of central nervous system lobes in octopus, viewed from the right side (bottom). Adapted from 11. Figure 1. Overview of cephalopod brain anatomy organization. A) Location and 3D rendering of major structures in the Octopus brain. B) Sagittal section displaying lobes of the SEM and SUB, from Shigeno et al., 2018. C) Diagram of major structures in the octopus brain, viewed from the front. Modified from Young 1965a The nervous system of Nautilus. D) Diagram of octopus brain organization seen from above, adapted from Young 1971 Anatomy of the nervous system of Octopus vulgaris. E) Diagram of central nervous system lobes in octopus, viewed from the right side. Adapted from Young 1971 Anatomy of the nervous system of Octopus vulgaris. 19 The cephalopod nervous system contains approximately 25 differentiated lobes which can be grouped into systems based on their specialized functions10,11,17,19,19–22,22–25,25. In octopuses, these functional systems are: 1) learning and memory (vertical and superior frontal lobe), 2) chemosensory and tactile learning and memory (inferior frontal lobe), visual processing (optic lobes), lower visuo-motor control (peduncle lobes), higher motor control for sensory processing and behavior (SEM), and lower motor control for funnel, arm, and mantle movement (SUB). From hatching to adulthood, the number of neurons in the central brain grow exponentially from 200,000 to 200 million26,27, with morphological changes in the brain reflecting their adaptive behavior28. For example, the vertical and subvertical lobes are part of tactile and visual learning11 and increases from hatching to adult, and the subfrontal lobe, which is involved in tactile learning29 also increases in size from hatching to juvenile and adult, reflecting the growth and usage of the arms. The optic lobes remain the largest structures of the central nervous system and, while it continues to grow through the lifespan of the animal, at hatching its parts and organization resemble what would be expected in the adult13. At hatching, the anterior basal and peduncle lobes, both of which fall within the cerebellum-like visuo- motor control functional system, are proportionally larger, given demands for prey capture along the water column. Specifically, the anterior basal regulates posture and movement of head and eyes while swimming11, while the peduncle lobes are involved in locomotion based on visual cues11,23. In the SUB, the buccal lobe, which is involved in prey capture and feeding30 and grows from 8% of total volume of the hatching to 18% in the adult as it is involved in lower motor control of the arms28, while the chromatophore lobes are not differentiated at hatching31, despite forming 5% of total brain volume in later development, similar to what has been described as a “metamorphosis”32–34. Like vertebrates, octopuses utilize many visually guided behaviors in their everyday life, such as navigating their environments and engaging in prey capture and predator avoidance (Reviewed in 35). Octopus vision is often referred to as a textbook example of convergent evolution, since both vertebrates and octopuses developed camera-type eyes36–38, which focus light through a lens onto a layer of cells called the retina13,39, despite these two lineages diverging over 500 million years ago. Uncannily, the anatomical resemblances of their camera eye propagate to the cellular 20 level as well—to the extent that the term “deep retina” was coined by Santiago Ramon y Cajal in reference to the octopus visual system. While the morphology and presumed synaptic connections, based on histology, of the octopus central nervous system has been described11,13, technical limitations have prevent a thorough deconstruction of other critical features that define neural circuits in the octopus visual system and across the rest of their brain. Goals of Dissertation In this dissertation, I will describe the work that I have done to characterize the functional identities of cells in the octopus visual system as well as the molecular architecture of the entire octopus central nervous system. In Chapter II, I will describe a protocol established to obtain the first calcium-evoked unit responses from the octopus visual system by applying two-photon imaging on an ex vivo preparation of the brain. We found evidence of retinotopic organization of responses to light (ON) and dark (OFF) spots as well as identified spatial tuning properties to ON and OFF that may be suggestive of environmental demands. In Chapter III, I will characterize the molecular identities of cells found in the octopus visual system by combining single cell RNA-sequencing (scRNA-seq) with multiplexed RNA fluorescence in situ hybridization (FISH) of top differentially expressed genes from each of the cell type clusters. We identified six major cell classes based on neurotransmitter and neuropeptide usage as well as a large population of immature neurons, which is unsurprising given that the octopus brain continues to grow throughout its lifespan. In Chapter IV, I expand our understanding of molecular architecture beyond the visual areas of the octopus brain by analyzing gene expression across each of the lobes found in the central nervous system. We generate the first mapping of brain organization in this species using serial sections stained with Hematoxylin & Eoisin (H&E), and we further characterize these regions by performing RNA FISH on 40 genes, including classical neurotransmitters, neuropeptides, and developmental markers. As a result, we produce the first brain-wide gene expression mapping in octopuses. 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Endeavour 28, 92–99. https://www.zotero.org/google-docs/?broken=6ADVGz https://www.zotero.org/google-docs/?broken=6ADVGz https://www.zotero.org/google-docs/?broken=6ADVGz https://www.zotero.org/google-docs/?broken=6ADVGz https://www.zotero.org/google-docs/?broken=fcx758 https://www.zotero.org/google-docs/?broken=fcx758 https://www.zotero.org/google-docs/?broken=fcx758 https://www.zotero.org/google-docs/?broken=fcx758 https://www.zotero.org/google-docs/?broken=fcx758 https://www.zotero.org/google-docs/?broken=QQFZnd https://www.zotero.org/google-docs/?broken=QQFZnd https://www.zotero.org/google-docs/?broken=WOHOgO https://www.zotero.org/google-docs/?broken=WOHOgO https://www.zotero.org/google-docs/?broken=WOHOgO https://www.zotero.org/google-docs/?broken=WOHOgO https://www.zotero.org/google-docs/?broken=WOHOgO https://www.zotero.org/google-docs/?broken=WOHOgO https://www.zotero.org/google-docs/?broken=WOHOgO https://www.zotero.org/google-docs/?broken=eKDd8e https://www.zotero.org/google-docs/?broken=eKDd8e https://www.zotero.org/google-docs/?broken=eKDd8e https://www.zotero.org/google-docs/?broken=eKDd8e https://www.zotero.org/google-docs/?broken=eKDd8e https://www.zotero.org/google-docs/?broken=qFsdZ4 https://www.zotero.org/google-docs/?broken=qFsdZ4 https://www.zotero.org/google-docs/?broken=qFsdZ4 https://www.zotero.org/google-docs/?broken=qFsdZ4 https://www.zotero.org/google-docs/?broken=qFsdZ4 https://www.zotero.org/google-docs/?broken=NJ1MBd https://www.zotero.org/google-docs/?broken=NJ1MBd https://www.zotero.org/google-docs/?broken=NJ1MBd https://www.zotero.org/google-docs/?broken=NJ1MBd https://www.zotero.org/google-docs/?broken=M1sapI https://www.zotero.org/google-docs/?broken=M1sapI https://www.zotero.org/google-docs/?broken=M1sapI https://www.zotero.org/google-docs/?broken=M1sapI https://www.zotero.org/google-docs/?broken=u4Nutp https://www.zotero.org/google-docs/?broken=u4Nutp https://www.zotero.org/google-docs/?broken=u4Nutp https://www.zotero.org/google-docs/?broken=u4Nutp https://www.zotero.org/google-docs/?broken=u4Nutp https://www.zotero.org/google-docs/?broken=u4Nutp https://www.zotero.org/google-docs/?broken=msfl6C https://www.zotero.org/google-docs/?broken=msfl6C https://www.zotero.org/google-docs/?broken=msfl6C https://www.zotero.org/google-docs/?broken=3PAyVF https://www.zotero.org/google-docs/?broken=3PAyVF https://www.zotero.org/google-docs/?broken=Y8PwGJ https://www.zotero.org/google-docs/?broken=Y8PwGJ https://www.zotero.org/google-docs/?broken=Y8PwGJ https://www.zotero.org/google-docs/?broken=Y8PwGJ 24 FUNCTIONAL ORGANIZATION OF VISUAL RESPONSES IN THE OCTOPUS OPTIC LOBE * This chapter contains previously published co-authored material. JR Pungor, VA Allen, JO Songco-Casey, and CM Niell. Current Biology, Volume 33, Issue 13, 10 July 2023. Author contributions: J.R.P. and C.M.N. conceived the project and designed experiments. J.R.P. led the project and performed experiments. V.A.A. and J.O.S.-C. both optimized the experimental protocol and performed experiments, contributing equally. C.M.N. and J.R.P performed data analysis. All authors contributed to the writing of the manuscript. Introduction Cephalopods evolved large and complex brains independently from the rest of the animal kingdom. Like vertebrates, cephalopods also evolved camera-type eyes that focus a high resolution image onto a retina1. Together, their large brain and camera-type eyes implement a sophisticated visual system, which mediates a wide range of advanced visually-based behaviors2, including prey capture and predator avoidance3,4, identifying mates5,6, spatial navigation7,8, and a remarkable ability to rapidly camouflage to their surroundings9–12. However, because the cephalopod brain evolved independently from that of other highly visual species, the neural organization of their visual system is dramatically different. Anatomical studies have delineated the morphology and structural connectivity of neurons in the cephalopod retina and optic lobes13–20. Unlike vertebrates, the cephalopod retina contains only photoreceptors, which send axons out of the retina into the optic lobes of the brain (Figure 1A, C). The optic lobes comprise roughly two thirds of the centralized nervous system and are where most of the visual processing in the cephalopod brain is thought to occur12. The outer optic lobe is a layered structure (Figure 1D, E), with two cell body layers, termed the outer granular (OGL) and inner granular layer (IGL), surrounding a layer of processes, the plexiform layer (Plex), https://paperpile.com/c/WfZi5S/vNtKB https://paperpile.com/c/WfZi5S/XO0x2 https://paperpile.com/c/WfZi5S/qr5TB+wNwuF https://paperpile.com/c/WfZi5S/JQAVR+hntwT https://paperpile.com/c/WfZi5S/KVejQ+i1n8P https://paperpile.com/c/WfZi5S/OE4BT+zHTJi+3se42+b4fLi https://paperpile.com/c/WfZi5S/SyHir+1X1I1+qwVTS+phxy3+prmh7+f8oxI+VvOYI+BTYoD https://paperpile.com/c/WfZi5S/b4fLi 25 where photoreceptor axons terminate. Together these were termed the “deep retina” due to their resemblance to the layers of the vertebrate retina13,15. The center of the optic lobe, the medulla (Med), consists of clusters of cell bodies arranged in a tree-like structure surrounded by neuropil21. Recent transcriptomic studies have revealed a rich diversity of cell types within the optic lobe, as well as extensive sub-laminar organization22–25. Figure 1. Experimental paradigm for calcium imaging of visual responses in the optic lobe A) Image of a juvenile Octopus bimaculoides. The central brain is shown in burgundy, and one optic lobe is outlined in white. B) Schematic of the experimental set-up. A projector is used to present visual stimuli on the side of the recording chamber, with the preparation underneath the objective of a two-photon microscope on an adjustable platform. C) Illustration of octopus visual system anatomy. Bundles of photoreceptor projections exit the back of the eye (left), decussate vertically, and enter the optic lobe (right) in a retinotopic manner. In the cutaway, the layered structure of the optic lobe can be seen, as it is in our imaging planes. Dorsal, posterior, and medial axes are shown in the key. D) Coronal section of the center of the octopus optic lobe, stained with DAPI to illustrate the overall anatomy of the layers, which are labeled as in Figure 1E. Dorsal-ventral and lateral-medial axes are shown in the key. E) Simplified illustration of the anatomy of the layers of the optic lobe. Color code for layers also applies to Figure 1C, F. F) Mean fluorescence image of calcium indicator loading across a horizontal optical section of the optic lobe, as shown in the green square in 1C, with layers delineated by dotted lines. Inset shows layers in color overlay. Lateral- medial and anterior-posterior axes are shown in the key. Early studies of photoreceptors in the cephalopod eye provided an initial description of visual processing at the input stage26,27. Like most other invertebrates28, cephalopods have rhabdomeric photoreceptors that depolarize in response to increases in light (ON responses)29, in contrast to vertebrate photoreceptors that depolarize in response to decrements in light (OFF responses). https://paperpile.com/c/WfZi5S/SyHir+qwVTS https://paperpile.com/c/WfZi5S/j7Epm https://paperpile.com/c/WfZi5S/IzmAu+FUHMi+fWXWD+E5qfm https://paperpile.com/c/WfZi5S/eug4g+CcZGE https://paperpile.com/c/WfZi5S/MPwnI https://paperpile.com/c/WfZi5S/3pVhP 26 Nearly all species of cephalopods, including octopuses, only express one type of opsin in their photoreceptors and are therefore thought to be monochromats26,30, consistent with behavioral findings31,32. Electrophysiological recordings from the retina have demonstrated ON-center receptive fields and indications of lateral inhibition33–36. However, little is known regarding neural responses beyond the photoreceptors35,37,38. In the visual system of many species, responses to increments and decrements of light are processed in separate ON and OFF pathways39, although the neural circuit mechanisms that give rise to these pathways, as well as their functional properties, can vary40,41. Likewise, many visual systems, though not all42, exhibit a topographic organization of visual space within the brain, known as retinotopy. However, no studies have addressed the neural representation of ON and OFF visual stimuli within the cephalopod optic lobe, or how this is organized topographically and transformed across the optic lobe layers. Here we developed techniques for two-photon calcium imaging of visually evoked responses in Octopus bimaculoides43, a promising model species for studying cephalopod vision44. We used this calcium imaging approach to measure how spatial and luminance information are represented in large-scale neural responses, and to determine how these responses are organized within the optic lobe. Results Calcium Imaging of Stimulus-Specific Visual Responses in the Optic Lobe Historically, electrophysiological recordings in the cephalopod brain have been technically challenging, and methods to express genetically encoded calcium indicators are not yet available in cephalopods. Here we employed a calcium imaging approach using an injection of a synthetic calcium indicator, Cal-520 AM-ester, to measure large-scale neural activity in the octopus optic lobe. Our general approach was adapted from techniques previously used to measure visual responses in the zebrafish optic tectum45, and the bolus loading method established for acetoxymethyl (AM) ester calcium indicators46. In this method, a membrane-permeable AM- ester form of a calcium indicator dye is injected into the brain, where it is taken up by cells and rendered fluorescent based on cleavage of the AM moiety by endogenous esterases. This https://paperpile.com/c/WfZi5S/PFJEr+eug4g https://paperpile.com/c/WfZi5S/7RRP4+5s66E https://paperpile.com/c/WfZi5S/OfJlG+tDJcW+lShKj+rvm6v https://paperpile.com/c/WfZi5S/lShKj+MewgC+r0e6N https://paperpile.com/c/WfZi5S/nX36G https://paperpile.com/c/WfZi5S/iTb1c+KWEAj https://paperpile.com/c/WfZi5S/2d6QV https://paperpile.com/c/WfZi5S/vGtCo https://paperpile.com/c/WfZi5S/SYcVk https://paperpile.com/c/WfZi5S/RuANO https://paperpile.com/c/WfZi5S/PsEXn 27 typically leads to dye loading across a region of 500-1000μm in the intact brain in zebrafish and mouse46. We injected Cal-520 AM-ester into one optic lobe of an ex vivo preparation comprised of the eyes and central brain of an octopus (Figure 1A) and imaged neural responses with a two-photon microscope, which provided optical access for recording across the optic lobe to a depth of up to 200μm (Figure 1B). The small sizes of the juvenile octopuses allowed us to image a large cross- section of their optic lobes spanning multiple layers, providing a view across both its tangential and laminar organization (Figure 1C-E), similar to how a cross-section of the top of the earth would both span longitude and latitude (tangential organization), as well as reveal the layers of the earth’s crust and mantle (laminar organization). Figure 1F shows loading of the fluorescent indicator across an optic lobe, with its different layers readily discernible. The full field of view of the microscope was 0.64mm2, but measurable fluorescent loading typically only covered 0.35 +/- 0.05mm2 (n=6 animals). The radius of the optic lobe at this age is roughly 1mm, with an approximately 4mm2 surface area, indicating we were imaging ~1/10th of the optic lobe area in a given experiment. Visual stimuli were displayed via a LCD projector onto a white diffusion filter mounted on the side of the chamber containing the preparation (Figure 1B). An adjustable platform allowed us to align the precise orientation of the eye so that receptive fields for a given imaging region were centered on the screen. This approach allowed us to present high-resolution stimuli across the visual field of one eye while simultaneously recording the responses across a region of the optic lobe. To obtain visually evoked responses, we initially used a stimulus of individual full contrast ON (light) and OFF (dark) rectangular spots (24x18deg) tiling the projection screen, presented in a random order on a 50% luminance gray background for 1sec duration. This stimulus elicited fluorescence responses in the optic lobe dependent on the location of the spot in the visual field (Figure 2, Video S1). ON and OFF responses were based directly on the activity during the stimulus duration for light and dark spots, rather than comparing onset and offset transients for https://paperpile.com/c/WfZi5S/PsEXn 28 ON stimuli as is done when light stimuli are presented on a dark background. Figure 2A shows the mean response, measured as the change in fluorescence divided by mean fluorescence (dF/F0) at each pixel across the optic lobe, over five repeated presentations of an ON spot at one location. The evoked activity, locked to stimulus onset, persisted throughout the one second stimulus period and was followed by a decay, consistent with calcium indicator dynamics. This activation map also suggests a temporal sequence of activity, with fluorescence signal first increasing rapidly in the superficial optic lobe, followed by more gradual and sustained response in the medulla. Figure 2B shows the mean response across the optic lobe during the stimulus presentation for ON spots in three neighboring locations. We found activation of distinct regions within the optic lobe to each location, indicating specificity for the location of the stimulus in visual space in a retinotopic manner. Finally, Figure 2C shows the response to an OFF spot at the same recording location as Figure 2B (middle), responding in approximately the same region, but deeper in the laminar structure of the optic lobe, in the IGL and medulla. Figure 2. Visually evoked responses in the optic lobe A) Mean timecourse of fluorescence response to a flashed ON spot at one location in the visual field (averaged over five stimulus repetitions), showing spatial organization and temporal dynamics. Duration of stimulus presentation is indicated by the red bar above the frames. Individual imaging frames are shown at 0.2sec intervals. B) Mean fluorescence response across the optic lobe to ON stimuli at three different horizontal locations, averaged across the stimulus duration for five repetitions. 0 0.05 0.1 0.15 0.2 0.25 dF/F0100um 100μm t=0 .2 sec .4 sec .6 sec .8 sec 1 sec 1.2 sec 1.4 sec 1.6 sec 1.8 sec 2 sec 2.2 sec 2.4 sec 2.6 sec CB A 100μm L M PA L M PA 29 C) Mean fluorescence response to an OFF stimulus at the same location as H (middle), averaged across the stimulus duration for five repetitions. These results demonstrate that our calcium imaging approach allowed us to measure stimulus- specific visual responses, and provide initial support for both retinotopic and laminar organization of responses. To probe the specificity and spatial organization of visual responses more systematically, we next performed mapping of spatial receptive fields using a sparse noise stimulus. Spatially Localized ON and OFF Receptive Fields We used a sparse noise stimulus adapted from47 to calculate ON and OFF receptive fields. The stimulus consisted of frames of ON and OFF circular spots of three different sizes (radius = 3, 6, 12deg) in a randomly distributed pattern, along with randomly interleaved ON or OFF full-field frames (Figure 3A). Each frame was presented for 1sec over a total recording time of 10mins. This sparse noise stimulus elicited robust and spatially localized fluorescence responses across the optic lobe, as demonstrated in Figure 3B and Video S2. Figure 3. ON and OFF receptive fields mapped with a sparse noise stimulus A) Example frames from the sparse noise stimulus used to map receptive fields. Frames were presented consecutively in a randomized order for 1sec each. B) Traces of fluorescence activity at 32 locations across the optic lobe in response to the sparse noise stimulus. https://paperpile.com/c/WfZi5S/4HxFe 30 C) RFs from four example units, two each for ON (top) and OFF (bottom) components of the stimulus. Note that because these units are from within a single imaging field of view, the RFs are in the same vicinity of visual space, consistent with retinotopic organization. D) Histogram of RF sizes for ON and OFF stimuli (n=6 animals). E) Location of units with RFs for ON (red), OFF (blue), or both (magenta) in one session across the optic lobe. F) Fraction of units overall with significant RFs for ON and OFF across the layers of the optic lobe (n=6 animals). For analysis, we selected individual regions of interest (ROIs), 20x20μm, centered on peaks in the mean fluorescence image above a baseline fluorescence threshold, to exclude regions that were not loaded with calcium indicator. This identified ~500-1000 ROIs spaced across each of the multiple layers of the optic lobe captured within each imaging field (e.g Figure 3E). We selected this approach, rather than extracting responses specifically from cell bodies as typically performed for calcium imaging in vertebrates, both due to the challenge in localizing signals to individual cells in tightly packed cell body layers and the fact that, in invertebrates, much of the neural signal is localized to processes within the neuropil. We refer to each ROI as a unit, denoting a specific location within the optic lobe, rather than a single neuron. This analysis allowed us to map how visual information is represented at locations across the optic lobe. As noted in the Discussion, single-cell or cell-type specific recordings will likely be needed to directly probe individual cell tuning properties. We computed receptive fields (RFs) for each unit using reverse correlation based on the evoked dF/F0 fluorescence signal for each frame of the sparse noise stimulus, excluding the full-field flashes (see Methods). We performed this separately for the ON (light) and OFF (dark) components of the stimulus to avoid cancelation of positive and negative stimulus contrast for units that responded to both polarities. This revealed spatially localized RFs for both ON and OFF stimuli, as shown by examples in Figure 3C. RFs were generally circularly symmetric by visual inspection, so we fit RFs to a Gaussian model to determine their size and location within visual space. Across experiments, 59 +/- 26% of all units had a RF significantly above background as determined by their z-scored response. The Gaussian model provided a good fit to the RFs, with a pixel-wise correlation between the measured RF and model fit of 0.85 +/- 0.07 for ON RFs and 0.74 +/- 0.14 for OFF (mean +/- s.d.) The lower correlation for OFF response likely reflects noisier RF estimates due to the lower response amplitude in OFF units (see Figure 31 4 below), but in both cases the vast majority of variance in the RF was explained by the Gaussian model. The RF radius, based on sigma of the Gaussian fit, was 5.7 +/- 0.6deg for ON, and 7.4 +/- 0.6deg for OFF (p=0.31 for ON vs OFF) (Figure 3D). Note that this is likely an overestimate of the RF size of individual neurons, since the response of each unit within the lobe represents the summed response of a number of individual neurons. Figure 4. Retinotopic organization of visual responses in the octopus optic lobe A) Example mapping of RFs in the optic lobe of responses to both ON (left) and OFF (right) stimuli. Areas are colored by the position of their RFs along the elevation (top) and azimuth (bottom) as shown by the color scale bars (degrees of visual space). Based on the position of the octopus eyes, elevation corresponds to the dorsal-ventral axis of the animal’s body, and azimuth corresponds to the anterior-posterior axis of the animal’s body. B) Scatter plot of RF location for elevation (top) and azimuth (bottom) versus unit location within the optic lobe, for both ON and OFF responses. Adjacent groups of cells responded to adjacent areas of the visual field. C) Mean coefficient of determination for elevation and azimuth maps across all recordings (n=6 animals). D) Mean scatter in RF location for elevation and azimuth, across all recordings (n=6 animals). Dashed line shows chance level based on a shuffle control. In each experiment, the measured RFs only subtended a restricted region of the visual field, consistent with a retinotopic organization and the fact that we are only imaging a limited extent of the optic lobe area (roughly 1/10th, as described above). We calculated that an area of 430 +/- 290deg2 (mean +/- s.d.) of the visual field that was covered by the RFs in each experiment. For context, the projection screen was 5400deg2 (90x60deg), so the fraction of visual space represented (~1/12th) corresponds well to the fraction of the optic lobe imaged. 32 We next examined the distribution of ON and OFF responses across the optic lobe to determine where the pathway for processing each arises. Figure 3E shows all units in an example recording labeled based on whether they had a RF for only ON (red), only the OFF (blue) component, or for both components of the stimulus (magenta). While ON responses are distributed throughout the lobe, OFF responses are largely restricted to the deeper layers of the IGL and medulla. To quantify this, we calculated the fraction of ON and OFF RFs in each layer across recordings (Figure 3F), confirming that OFF responses primarily emerge in the IGL and are strongest in the medulla. The sequential emergence of OFF responses relative to ON is consistent with the fact that photoreceptor axons in cephalopods, which mainly terminate in the superficial layers of the optic lobe (Plex), respond to increments of light, and demonstrates that the OFF processing pathway originates in neurons further along the visual processing pathway. We found both ON and OFF responses within the same fields of view, corresponding to the same region of visual space, suggesting that variations are primarily due to depth. However it remains possible that there could also be variations in the ON/OFF distribution across the visual field in other regions of the optic lobe. Retinotopic Organization of the Optic Lobe To determine if there was a retinotopic organization of visually evoked responses in the octopus optic lobe, we labeled each unit according to the location of its RF, based on the center of the Gaussian fit described above. As shown in Figure 4A, we found clear retinotopic progression for ON and OFF responses, along both the elevation and azimuth of the visual field, resulting in a map of visual space across the optic lobe. This is further demonstrated in Figure 4B, which shows the high degree of correlation between the unit’s physical location across the optic lobe with its RF location in visual space. The retinotopic maps of ON and OFF RFs were also aligned in regions of the lobe that were responsive to both (Figure 4B). Note that, as described above, the imaged retinotopic map does not span the full visual space of the projector screen, but is typically restricted to roughly 20x20deg (mean 430deg2), due to the fact that we were only imaging a subregion of the optic lobe. 33 We next quantified the retinotopic organization in each experiment by performing a linear regression between the RF elevation/azimuth in the visual space of all responsive units and their x/y location within the optic lobe. We used both the x and y location of the units to predict each RF parameter, since the visual axes were not always aligned to the x and y axes of the imaging plane depending on the orientation of the preparation. This fit resulted in a mean coefficient of determination greater than 0.8 for both ON and OFF maps across experiments (Figure 4C), confirming robust retinotopy. We also computed the scatter of RF locations (i.e. how much RF locations deviate from a linear retinotopic progression), based on the residuals from the fit, which demonstrated that individual unit’s RFs have scatter of less than 2 degrees (Figure 4D). Finally, the slope of the RF fit determines the magnification factor of the map (i.e. how much the RF location changes for a given distance in the brain), with a mean progression of 21.9 +/- 1.4 μm/deg in elevation and 25.0 +/- 3.0 μm/deg in azimuth. Together, these data provide the first functional demonstration of a retinotopic organization of visual information within the cephalopod brain. Size Selectivity in ON and OFF Pathways Across Layers of the Optic Lobe To further examine visual response properties and their organization within the octopus optic lobe, we next calculated size tuning of units based on their evoked responses to spots of different sizes in the sparse noise stimulus. For each unit with a significant RF, we determined the center of its ON or OFF RF from the Gaussian fit, and computed the mean dF/F0 response timecourse when spots of different sizes appeared in this RF. We limited this analysis to units with significant RFs, based on z-score as described above, because it is only meaningful for units that have a defined RF location. Figure 5A shows the mean timecourse of response during the 1sec stimulus presentation for each size stimulus, including full-field flash, for all units across experiments, divided into their layer within the optic lobe. In order to accurately represent the relative magnitude of responses across layers, given the differential distribution of ON and OFF units (Figure 3E), we weighted these mean traces by the respective fraction of responsive units within each layer. All responsive 34 populations showed sustained activity throughout the 1sec stimulus presentation, although with diverse temporal dynamics. We note that because our measurements represent the activity of multiple neurons in each unit, these dynamics could also represent the summation of populations with heterogeneous temporal responses, for example both transient and sustained responses. Figure 5. Size selectivity and temporal dynamics across the layers of the optic lobe A) Mean timecourse of ON (top) and OFF (lower) responses for each stimulus size, separated by layers of the optic lobe. Response for each layer and luminance are weighted by fraction of units responsive. OGL did not show a significant response to OFF, and was omitted from this figure. Stimulus onset is at t=0 and each frame was presented for 1sec, as shown by gray bars on the x axis (n=6 animals). B) Mean size tuning curves for ON responses in each layer, normalized to the response to the smallest stimulus (n=6 animals). C) Mean timecourse of unit responses, averaged across the three sizes of stimulus spots and normalized to the maximum response, for ON (Plex, IGL, Med) and OFF (Med) (n=6 animals). D) Mean rise time to half-maximum response from data in C. Plex ON IGL ON Med ON Med OFF Region 0 0.1 0.2 0.3 0.4 0.5 H al f-m ax ri se ti m e (s ec ) DC -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Seconds 0 0.2 0.4 0.6 0.8 1 1.2 No rm al iz ed d F/ F0 Med OFF Plex ON IGL ON Med ON B 3 12 Full Field FlashStimulus size (deg) 0 0.2 0.4 0.6 0.8 1 1.2 N or m al iz ed d F/ F0 Plex IGL Med 3 deg 6 deg 12 deg Full field OGL IGL Med w ei gh te d dF /F 0 Plex 0 1 2 Seconds 0 0.02 0.04 0.06 0 1 2 0 1 2 0 1 2 0 1 2 0 0.02 0.04 0.06 0 1 2 0 1 2 O FF A w ei gh te d dF /F 0 O N 6 35 For ON stimuli (Figure 5A, top), there was a strong and rapid response in the Plex, which was approximately equal in amplitude for all stimulus sizes, as well as to the full-field flashes. However, as responses progressed deeper into the IGL and medulla, the sustained response increased for small stimuli but decreased for larger spots and full-field flashes, indicating size selectivity. Interestingly, the initial onset responses were similar across sizes, with the responses to different sizes diverging only after ~200msec. Figure 5B shows the size tuning curve of ON responses for each layer, based on the mean dF/F0 across stimulus duration, normalized to the response to the smallest stimulus size. These show a decrease in the relative response to larger stimuli in the IGL and medulla. Together, the responses to ON stimuli showed an emergence of size selectivity, both over time and across layers. In contrast, responses to OFF stimuli (Figure 5A, bottom) only appeared in deeper layers of the optic lobe (IGL and Med). There was no size suppression across different sized spots, in contrast to what was seen in the ON. Rather, responses to OFF spots of all sizes were roughly equal, leading to a relative bias toward large stimuli in OFF compared to ON. Strikingly, there was no response at all to the full-field OFF stimulus, despite responses to the range of OFF spot sizes and to full-field ON. These differences in spatial integration for ON and OFF suggest different processing pathways exist for these two luminance modalities, even in these early visual processing stages. Finally, we examined the mean timecourse of response onset to ON and OFF spots across the layers of the optic lobe (Figure 5C, D), revealing distinct temporal dynamics. ON responses increased rapidly in the plexiform layer, and then spread into the deeper regions of the IGL and Med. On the other hand, OFF responses were first seen predominantly in the medulla (Figure 5A, bottom panels), and had a slower rise time (Figure 5C, blue trace). We quantified this difference in timecourse based on the mean rise time to half-maximum response (Figure 5D). It should be noted that this metric represents the rate of increase of response rather than latency to first response, which was faster than our framerate as clear responses were already seen in the first frame following stimulus onset (Figure 5C). There was an approximately 100msec 36 difference in rise time for ON responses from Plex to IGL and medulla, and an additional 100msec difference within the medulla from ON responses to OFF responses (Figure 5D), consistent with a later emergence in the visual processing circuit. Discussion Octopuses represent an intriguing independent evolution of a complex nervous system. However, relatively little is known about how their brain functions at the neural level. Combining large scale two-photon calcium imaging with controlled visual stimuli, we were able to overcome technical challenges that previously hindered recordings of neural activity in cephalopods. The establishment of such recording techniques, and future improvements, will be essential for elucidating the computations performed in their visual system, as well as other aspects of sensory processing, cognition, and behavior in cephalopods. Using this calcium imaging approach, we measured response properties of populations of neurons within the octopus optic lobe, and began to identify what fundamental features of the visual world they encode, and how these emerge in the early stages of visual processing. We found similarities in visual processing between octopus and other species, such as a retinotopic organization of responses, highlighting potential fundamental principles for the organization of visual systems across the animal kingdom. We also identified unique organization of ON/OFF pathways and size selectivity, that may have arisen due to these animals' environmental constraints48 or distinct evolutionary trajectories49. These findings are the first to show visual processing dynamics across the layers of the octopus optic lobe and provide a foundation for studying the processing of more complex visual features. Spatial Organization of Response Properties in the Optic Lobe Although there have been previous studies of the anatomical organization of the octopus visual system, little is known about its functional organization. Based on the fashion in which the optic nerves from the eye were found to enter the optic lobe16,50,51, it was predicted that visual information would be retinotopically organized within the lobe, as it is in many, though not all42, visual systems across the animal kingdom. However, studies in the motor system of cephalopods https://paperpile.com/c/WfZi5S/ThJYv https://paperpile.com/c/WfZi5S/p7qtH https://paperpile.com/c/WfZi5S/phxy3+NKST5+1E911 https://paperpile.com/c/WfZi5S/2d6QV 37 demonstrated a surprising lack of somatotopy in their central brain, suggesting they may have evolved alternative, non-topographic architectures for representing spatial information52. In this study, we found that neural coding in the visual system of the octopus is indeed organized retinotopically, with aligned maps for responses to ON and OFF stimuli, demonstrating that the lack of topographic mapping previously observed in the motor system is not a general feature of cephalopod brain organization. Previous anatomical studies had suggested potential neural circuits across the layers of the octopus optic lobe that could implement sequential processing of visual input13,14,16, as in the vertebrate retina or fly visual system53. Our findings support these predictions, demonstrating that the temporal dynamics of visual responses in octopuses do in fact proceed sequentially across the laminar organization of their brain. This is accompanied by a transformation of the visual input, including the emergence of the OFF pathway, as well as an increase in size selectivity in the ON pathway. Our findings of differential response dynamics across distinct layers provide an initial framework for understanding the functional computations performed by the cephalopod visual system. Comparative Aspects of ON/OFF Pathways and Spatial Processing A key computation for any visual system is the ability to respond to both light and dark stimuli within a scene. Given that photoreceptors depolarize to only ON (invertebrates) or OFF (vertebrates) stimuli, there is a necessary computation to invert the polarity of the photoreceptor signal within the subsequent visual circuitry to achieve this. For vertebrates it is known that this inversion occurs at the photoreceptor to bipolar cell synapse54, while in Drosophila segregated ON and OFF responses emerge one synapse further from the photoreceptors, between the lamina and medulla55. Here we found that ON responses dominate in the primary input layer of the octopus optic lobe, the plexiform layer, corresponding to the fact that cephalopod photoreceptors depolarize to light increments. OFF responses only emerge initially in the IGL and are greatly increased in the medulla, suggesting a potential site for the sign inversion circuitry. We also found that OFF https://paperpile.com/c/WfZi5S/Ive76 https://paperpile.com/c/WfZi5S/SyHir+1X1I1+phxy3 https://paperpile.com/c/WfZi5S/lOhf4 https://paperpile.com/c/WfZi5S/cM8bq https://paperpile.com/c/WfZi5S/ohJf5 38 responses have a strikingly different profile from ON. Despite prominent responses to full-field ON stimuli across layers, there is a complete lack of response to full-field OFF stimuli. This suggests that the OFF pathway may emerge through a different mechanism than direct inversion of the photoreceptor input, which would yield responses to a full-field OFF stimulus. One possibility is that the OFF pathway receives input from a subset of ON neurons that have completely suppressed the response to a full-field stimulus. A more intriguing possibility is that the mechanisms that generate OFF responses may rely directly on boundaries between light and dark regions, which would explain why OFF responses are driven by localized dark stimuli (i.e. spots) that contain such edges, but not full-field stimuli, which do not. Additionally, we found differences in size selectivity for spots in the ON and OFF pathways (Figure 5A). While responses in the ON pathway decreased for larger spots, the responses to spots in the OFF pathway were roughly equal across the sizes of stimuli we measured. This implies a net bias toward the enhancement of responses to smaller stimuli in the ON pathway. Asymmetries in ON/OFF visual processing have been found in other species across the animal kingdom, and are thought to enhance ethologically relevant visual features to meet each animals’ specific visual demands56–60. The enhancement of responses to smaller stimuli in the ON pathway that we observed may be beneficial when processing visual scenes underwater, where light intensity is greatly attenuated by both absorption and scatter61. As a result, nearby objects, like potential prey, would tend to appear bright against a large, dark background. An OFF pathway biased towards larger stimuli might also aid in the detection of large, looming objects, which often represent predators. It will be interesting to see if such ON/OFF processing differences exist more broadly across cephalopods that occupy other ecological niches, particularly as these vary greatly in luminance levels and visual scene statistics62. Implications for Future Studies Our findings provide initial insight into how luminance and size information are processed within the different layers of the octopus optic lobe. However, both anatomical and https://paperpile.com/c/WfZi5S/oFVP+wpW7+3aCut+LeSwk+ey6Aw https://paperpile.com/c/WfZi5S/RSxcY https://paperpile.com/c/WfZi5S/SvRoe 39 transcriptomic studies22,24,25,63 have revealed a high degree of cell type diversity within these layers, so the bulk response properties we examined here undoubtedly mask a high degree of underlying functional diversity. Identifying more detailed response properties within the parallel pathways of diverse cell types in this system will likely benefit from methods to record using genetically encoded calcium indicators, not yet available in cephalopods to date. Such an approach would also help address the challenge in associating activity in neural processes, which often dominate in invertebrate neurons, with individual neurons or populations of neurons64. More broadly, future studies based on these findings and methodology could explore the range of feature selectivity in the visual system of octopuses, as has been studied in other species, such as motion processing, orientation selectivity, object recognition, and lateralization of visual responses65,66. Additionally, this approach can be used to study aspects of visual responses that may be specific to cephalopods, such as the ability to detect stimuli based on the polarization angle of light67, or to extract information from the visual scene for camouflage11. Further studies may continue to reveal how the cephalopod brain performs the computations that enable the remarkable visual capabilities of these enigmatic creatures. Methods Experimental Model and Subject Details All studies were conducted with approved protocols from the University of Oregon Animal Care Services, in compliance with the Association for Assessment and Accreditation of Laboratory Animal Care International guidelines. Animal husbandry and protocols were carried out in accordance with published guidelines for the care and welfare of cephalopods in the laboratory68,69. Octopus bimaculoides were obtained from the Cephalopod Culture Program at the Marine Biological Laboratory (Woods Hole, MA) and from Aquatic Research Consultants (Dr. Charles Winkler, San Pedro, CA). Animals used were 4-8 weeks old and of indeterminate gender. Octopuses were kept in a 250 gallon closed circulating seawater system, held at 22°C and illuminated on a 12/12hr day/night light cycle. Each animal was kept in an isolated enclosure https://paperpile.com/c/WfZi5S/fWXWD+IzmAu+E5qfm+x9Hwn https://paperpile.com/c/WfZi5S/2y9yK https://paperpile.com/c/WfZi5S/ZImDe+zZe4w https://paperpile.com/c/WfZi5S/Iraw3 https://paperpile.com/c/WfZi5S/3se42 https://paperpile.com/c/WfZi5S/zi4RP+vCjUQ 40 within the system, allowing for ample freedom to roam, while keeping them isolated from potential cannibalism from counterparts. Each enclosure contained items that provided shelter (large shells, tubes) and environmental enrichment (smaller shells, Legos, beads, rotated weekly). Animals were fed a mixed diet of frozen shrimp, clams, and fish, offered daily. Method Details Calcium Imaging Animals were deeply anesthetized in artificial seawater (ASW) (460mM NaCl2, 10mM KCl, 10mM glucose, 10mM HEPES, 55mM MgCl2, 11mM CaCl2, 2mM glutamine, pH 7.4) supplemented to contain 110mM MgCl2 at 13-15°C. When the animal was no longer responsive to a firm pinch test of the mantle, it was transferred to an oxygenated dish of a 30:70 mix of isotonic 370mM MgCl2 with ASW that was held between 13-15°C. Animals were then rapidly euthanized via decapitation and removal of the arm crown. A solution to dilate their pupils (10% phenylephrine HCl in ASW) was manually applied to the eyes. Dissection was performed to expose the brain and remove surrounding musculature and skin in order to reduce motion artifacts and increase optical access for recording. The ex vivo preparation of the central brain and both eyes was secured to a coverslip using cyanoacrylate (Vetbond, 3M). A dye solution of 1mM Cal-520 AM (AAT Bioquest), 2.5% Alexa Fluor™ 568 Hydrazide (Thermo Fisher), 8% dimethylsulfoxide, and 2% pluronic acid (AAT Bioquest) in ASW was injected into one of the optic lobes via a glass micropipette needle (Harvard Apparatus Cat. Num. 30-0038) using a pressure injector (ASI, Inc). Micropipettes with a tip diameter of 9μm were back filled with the dye solution via capillary loading. For each animal, three individual injection sites were used. Three 1sec pulse injections at 5PSI pressure, with a constant 1PSI back pressure, were performed along the track of each injection site, with each injection done closer to the surface of the lobe than the last by retracting the needle ~50μm between each. Injections were targeted to the superficial layers (IGL and superficial medulla) of the optic lobe to optimize dye delivery to areas that were optically accessible in the imaging set- up. After injection, the preparation was covered in a thin layer of 4% low melt agarose in ASW 41 (Sigma) to secure it and to minimize movement. This paradigm was adapted from previous work in zebrafish45, see also70. The preparation was kept in a recording chamber filled with ASW and continuously oxygenated via an airstone to maintain tissue viability71. The recording chamber consisted of a 7.6cmx7.6cmx5cm plastic box (TAP Plastics) where one side was replaced with a white diffusing glass (Edmund Optics, Cat. Num. 02-149) to serve as a projection screen for visual stimuli. The coverslip with the mounted preparation attached was secured to a custom-built rotatable platform within the recording chamber to allow for alignment of the preparation to the stimulus screen. The eye ipsilateral to the loaded dye was placed 2cm from the screen for recordings, with the contralateral eye facing away from the screen. The chamber temperature was monitored and held between 17-22°C. Due to the need for the calcium indicator to be taken up into cells and then for the AM moiety to be cleaved, resulting in fluorescence, the preparation was kept in the dark under the two-photon microscope for 30-45 minutes before recording began. During this time we periodically examined the preparation for fluorescence and visual responses using a brief (<1sec) presentation of a flashing full field white stimulus. Experiments began after ~30-45 minutes, when the fluorescent loading had plateaued and visual responses were apparent. Calcium imaging was performed with a two-photon microscope (Neurolabware Inc.), using a 16X Nikon CFI75 LWD objective, via the Scanbox software package for Matlab (MATHWORKS). Data were acquired at a 10Hz framerate, with an 800x800μm (796x796 pixel) field of view. Recordings were taken at 90 to 170μm depths from the dorsal surface of the optic lobe. Visual Stimuli Custom generated visual stimuli, rendered using the PsychToolbox package for Matlab72, were displayed with a pico LCD projector (AAXA Technologies) onto the diffusing glass on the side of the recording chamber. To avoid light from the stimulus entering the two-photon detection https://paperpile.com/c/WfZi5S/RuANO https://paperpile.com/c/WfZi5S/upENC https://paperpile.com/c/WfZi5S/J6UiH https://paperpile.com/c/WfZi5S/N1KIY 42 pathway, the projected light was passed through a 450/50 bandpass filter (Chroma Technology Corporation), avoiding overlap with the emission spectrum of the Cal-520 calcium dye and the bandpass 525/50 emission filter of the microscope. The stimulus bandpass filter also restricted the stimulus light to be within the known absorption spectrum of cephalopod photopigments26. Stimuli were gamma-corrected in software and presented at 60FPS. ON and OFF stimuli were presented as full contrast increments and decrements of light on a 50% luminance gray background. Initial mapping was performed using a stimulus consisting of full contrast ON (100% luminance) and OFF (0% luminance) rectangular spots (24x18deg) on a 6x4 grid spanning the projection screen, presented in a random order for a one second duration, for a total recording time of 5min. This stimulus was also repeated at the end of the experimental session to confirm stability and viability of the preparation. Full RF mapping was performed using a sparse noise stimulus, consisting of white and black spots (radius = 3, 6, 12 deg; density = 10%) on a gray (50% luminance) background, along with full-field white or black on 2% of frames. Each stimulus frame was presented for 1sec in a randomized order for a total duration of 10min. Preparations were kept in the dark for 10min between each stimulus set presentation. The results of presenting each stimulus set once to each of six animals are shown here. In some preparations additional stimuli were presented at the same or other recording locations but are not included in this study. Recordings reported here were taken 2-5.5 hours after injection. Quantification and Statistical Analysis Data Analysis Data analysis was performed using custom software in MATLAB. We applied a rigid alignment of imaging data using the sbxalign function in Scanbox (Neurolabware, Inc.). In order to detect large movements that were not corrected by the alignment algorithm, for each frame we calculated the pixel-wise correlation coefficient to the mean image. Frames with less that 90% correlation were discarded from further analyses. We calculated the fluorescence activity (dF/F0) at each pixel as the standard (F(t) - F0) / F0, where F(t) is the fluorescence intensity of the pixel on each frame and F0 is the median https://paperpile.com/c/WfZi5S/eug4g 43 fluorescence intensity of the pixel across the recording. To analyze local responses, we defined “units” as a 20μmx20μm wide square window, centered on local peaks within the mean fluorescence that were above the background fluorescence, to ensure that only areas with sufficient dye loading were analyzed. dF/F0 for each unit was calculated as the mean dF/F0 across pixels within the unit. Units were manually assigned to anatomical layers (OGL, IGL, Plex, and Med) based on location within the mean fluorescence image from the recording session. To analyze receptive fields (RFs), based on the sparse noise stimulus, we first calculated the evoked response, 𝑟(𝑡), for each frame as the mean dF/F0 across the one second duration of stimulus presentation, minus the mean dF/F0 in the preceding 300msec. RFs were calculated by reverse correlation between the each stimulus frame, 𝑠(𝑥, 𝑦, 𝑡), and the evoked response to that frame. 𝑅𝐹(𝑥, 𝑦) = -⬚ ⬚ " 𝑠(𝑥, 𝑦, 𝑡) ∗ 𝑟(𝑡) / -⬚ ⬚ " 𝑟(𝑡) We computed the z-score for each RF based on the maximum absolute value of the RF, divided by the standard deviation across pixels. We used a z-score of 5.5 as the threshold for significant responses. In order to analyze RF size and location, we fit each RF to a Gaussian function, defined as 𝑅𝐹#$"(𝑥, 𝑦) = 𝐴 ∗ 𝑒𝑥𝑝((𝑥 − 𝑥%)& / 2𝜎'& + (𝑦 − 𝑦%)& / 2𝜎(&) + 𝐵 We used 𝑥%, 𝑦% as the receptive field center, and computed RF radius as (𝜎'⬚+𝜎(⬚)/2. To quantify topographic maps, we performed a linear regression for each recording for responses to both azimuth and elevation, as a function of each unit’s location within the optic lobe from the Gaussian fit, and used the coefficient of determination and standard deviation of residuals (scatter) as metrics of retinotopy. Statistical Analysis 44 Statistical tests for comparison of responses across populations within the optic lobe were performed using a t-test. To account for the nested design (many units per recording) of this analysis, all statistical tests were performed at the level of recordings, rather than total number of units recorded. Summary statistics in text and figures are presented as mean +/- standard error, unless otherwise noted. 45 References 1. Packard, A. (1972). Cephalopods and Fish: The Limits of Convergence. Biol. Revs. 47, 241–307. 2. Hanlon, R.T., and Messenger, J.B. (2018). Cephalopod Behaviour 2nd ed. (Cambridge University Press). 3. 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