Yemini, E.I. et al. (2017) International Worm Meeting "Whole-Brain Imaging with Single-Neuron Identity in C. elegans."

Venkatachalam, V., Yemini, E.I., Hobert, O., Samuel, A.D.

[International Worm Meeting, 2017]

Stereotypy of the C. elegans nervous system affords single-neuron registration across animals, and consequently, robust statistics for neurobehavioral coding and transcriptomics. Fast methods of whole-brain imaging in worm exist, but unfortunately determining the identity of neurons within these volumes remains a bottleneck - requiring a long, difficult, ad hoc process. We have developed a landmarked strain for whole-brain neural identification, NeuroPAL (a Neuronal Polychromatic Atlas of Landmarks). Each neuron is assigned an invariant fluorophore barcode such that color and position specify unambiguous neural identity via the unique 3-tuple (color, position, ganglion). The GFP channel is preserved for reporters of neural activity (GCaMP) and transcriptomics (GFP). Our landmark strain employs 5 fluorophores. To visualize this strain we have used a new microscope, developed by the Samuel lab, with the capability of imaging 9 unique fluorescence channels generated by 4 excitation lines and 4 emission bands. This microscope acquires whole-brain volumes, of 4 fluorophores, simultaneously, at 10Hz. Together, these two innovations permit fast whole-brain imaging, with single-neuron identity and neuronal registration, across an animal populace.

Yemini, E.I. et al. (2019) International Worm Meeting "Whole-Brain Functional and Molecular Connectomics using the NeuroPAL."

Hobert, O., Mena, G.E., Nejatbakhsh, A., Samuel, A.D.T., Paninski, L., Sun, R., Yemini, E.I., Venkatachalam, V., Lin, A., Varol, E.

[International Worm Meeting, 2019]

The identification of neuronal patterns of activity, gene expression, and mutant effects represents a major challenge when investigating neural circuitry. We introduce a multicolor C. elegans strain, called the NeuroPAL (a Neuronal Polychromatic Atlas of Landmarks), to resolve unique neural identities in whole-brain images. All NeuroPAL worms share an identical color map, permitting a complete, unambiguous determination of individual neuron names when using GCaMP/GFP/CFP/YFP reporters. The NeuroPAL is accompanied by our software for fully-automated neural identification. For functional connectomics, we employ the NeuroPAL and a neural-activity sensor (GCaMP6s) to delineate the neural circuitry activated by the deterministically asymmetric ASE gustatory neurons (in response to NaCl) and the stochastically asymmetric AWC olfactory neurons (in response to butanone and 2,3-pentanedione). We show that these stimuli activate considerable shared circuitry, uncover a wealth of neurons responding to both modalities, and explore the extent of downstream asymmetries. We then assemble a broad base of whole-brain experiments into a preliminary functional connectome for the worm. For molecular connectomics, we employ the NeuroPAL and GFP-based reporters to identify expression for the whole family of metabotropic classical-neurotransmitter receptors (cholinergic GAR's, GABAergic GBB's, and glutamatergic MGL's). We find that these metabotropic receptors can account for 65% of the structural connectome's synaptic communication, with as much as 36% extra-synaptic transmission activating metabotropic GABAB receptors. Combining our results with the previously published ionotropic GABAA receptor expression, we complete a comprehensive view of the GABAergic connectome and discover, surprisingly, that metabotropic reception appears to be the primary route for GABA communication. Lastly, we illustrate the utility of the NeuroPAL for unbiased mutant screens, finding that the broadly-expressed transcription factor EOR-1 engineers GABAergic fate in RMED and RMEV. Thus we demonstrate the power of the NeuroPAL as a tool for decoding whole-brain functional and molecular connectomics.

Endo, Yuto et al. (2021) International Worm Meeting "Whole-brain imaging with neuronal identities to elucidate the mechanism of a sensory processing"

Miyachi, Miyu, Kimura, Kotaro, Wen, Chentao, Endo, Yuto

[International Worm Meeting, 2021]

A neural circuit in the brain processes sensory stimuli such as an odor to elicit a motor command. To understand the mechanism of such a sensory processing, a number of studies have sought to identify a neural circuit which processes a specific sensory stimulus (functional neural circuit). For identification of a functional neural circuit, it is required to conduct two types of experiments throughout the brain: 1) locating neurons that respond to the stimulus, and 2) identifying their cell types (cell-IDs) to estimate their connectivity based on anatomical connections. Locating stimulus-responsive neurons can be achieved by whole-brain calcium imaging, which record all neuronal activities throughout the brain. However, due to the lack of way to efficiently identifying cell-IDs, it has been difficult to identify a complete functional neural circuit with whole-brain imaging and cell identification. To overcome the limitation, the method for cell identification of all neurons, called NeuroPAL, was developed in C. elegans (Yemini et al., Cell, 2020). Together with the connectome information as well as the records of all neuronal activities, we are now able to estimate a functional neural circuit based on cell-IDs of stimulus-responsive neurons. To identify a complete functional neural circuit by integrating those techniques, we established a whole-brain imaging system combined with efficient cell identification system using NeuroPAL. This system consists of a 3D confocal microscopy, a multi-color imaging system, a microfluidic device, and a pipeline of an image analysis (Chronis et al., Nat. Meth., 2007; Wen et al., eLife, 2021). By using this system, we successfully recorded activities of most head neurons simultaneously with an information of each cell-ID. Now we are identifying a set of odor-responsive neurons using this system with stimulation by a repellent odor 2-nonanone (Kimura et al., J. Neurosci., 2010). This study aims to identify a complete functional neural circuit for specific sensory processing pathways and it may provide clues for how a sensory stimulus is processed in the whole of a neural circuit. We thank Ev Yemini and Oliver Hobert for the help on NeuroPAL.

Yu, Xinwei et al. (2021) International Worm Meeting "Fast deep learning correspondence for neuron tracking and identification in C. elegans using semi-synthetic training"

Sharma, Anuj K., Linderman, Scott W., Randi, Francesco, Yu, Xinwei, Leifer, Andrew M., Creamer, Matthew S.

[International Worm Meeting, 2021]

The nervous system of the nematode C.elegans is well characterized, such that each of the 302 neurons is named and has stereotyped locations across animals(Witvliet et al., 2020). The capability to find corresponding neurons across animals is essential to investigate neural coding and neural dynamics across animals. Despite the worm's overall stereotypy, the variability in neurons' spatial arrangement is sufficient to make predicting neural correspondence a challenge. We present an automated method to track and identify neurons in C. elegans, called "fast Deep Learning Correspondence" or fDLC, based on the transformer network architecture(Vaswani et al., 2017). The transformer has shown great success in natural language processing tasks by modeling the dependencies between words in a sentence. We reasoned this same architecture would be well-suited to extract spatial relationships between neurons in order to build a representation that facilitates finding correspondence to neurons in a template worm. The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals via transfer learning. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL(Yemini et al., 2020). Using only position information, the method achieves 80.0% accuracy at tracking neurons within an individual and 65.8% accuracy at identifying neurons across individuals. Accuracy is even higher on a published dataset(Chaudhary et al., 2021). Accuracy reaches 76.5% when using color information from NeuroPAL. Unlike previous methods, fDLC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications. Witvliet D, Mulcahy B, Mitchell JK, Meirovitch Y, Berger DR, Wu Y, et al.Connectomes across development reveal principles of brain maturation in C.elegans. bioRxiv. 2020;doi:10.1101/2020.04.30.066209. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al.Attention is All You Need; 2017.Available from:https://arxiv.org/pdf/1706.03762.pdf. Yemini E, Lin A, Nejatbakhsh A, Varol E, Sun R, Mena GE, et al. NeuroPAL: A multicolor atlas for whole-brain neuronal identification in C. Elegans. Cell.2020;doi:10.1016/j.cell.2020.12.012. Chaudhary S, Lee SA, Li Y, Patel DS, Lu H. Graphical-model framework for automated annotation of cell identities in dense cellular images. eLife.2021;10:e60321. doi:10.7554/eLife.60321.

Chaudhary, S. et al. (2019) International Worm Meeting "A strategy for neuron identification in whole-brain videos."

Lee, S., Patel, D., Chaudhary, S., Lu, H.

[International Worm Meeting, 2019]

A current bottleneck in generating meaningful interpretation of the neuron activity dynamics extracted from C. elegans whole-brain videos is determining the identity of neurons. Previous strategies to determine neuron identity include, manual comparison of images with an atlas [1] and polychromatic barcoding of neurons [2]. The former strategy is labor intensive, prone to human bias and error, and requires expert supervision. The latter strategy requires custom designed microscope with several excitation-emission channels that may not be available to all researchers. Here we present an alternative strategy based on neuronal landmarks for assisting researchers in unbiased identification of neurons. We developed a framework [3] that uses known geometrical relationships among positions of neurons and automatically generates a probabilistic identity for each neuron in whole brain stacks. We tested the effect of neuron position variability on prediction accuracy by perturbing ideal data (atlas data) to mimic real images. Next, we established that either spatially distributed landmarks or landmarks in lateral ganglion will provide optimal prediction accuracy. Guided by our analysis, we generated whole-brain imaging strains with CyOFP-labeled neuronal landmarks in the head, which are easily identified in each worm. They establish a coordinate system with which neurons are identified by matching to the atlas. We also validated the automatic prediction accuracy on many real worm data sets. Further, using CyOFP enables 3-channel (GCaMP, RFP and landmarks) imaging with only 2 emission-excitation filter sets that are commonly available, thus freeing a channel for other purposes such as optogenetic manipulations. We use this strategy to compare neurons' responses to chemical stimulation in different individuals, and show how experimental context can be included in the prediction framework as additional constraint. We expect that our algorithm will help realize the full potential of whole-brain imaging techniques by enabling complex experiments and analyses using methods that require neuron identity. References [1] Kato S, et al. Cell. 2015;163: 656-669. doi:10.1016/j.cell.2015.09.034 [2] Personal communication (Ev Yemini and Oliver Hobert) [3] Lafferty J, et al. ICML '01 Proc Eighteenth Int Conf Mach Learn. 2001;8: 282-289. doi:10.1038/nprot.2006.61

Scholz, Monika et al. (2019) International Worm Meeting "Predicting locomotion from whole-brain neural dynamics in freely moving animals."

Randi, Francesco, Leifer, Andrew, Yu, Xinwei, Shaevitz, Joshua, Linder, Ashley, Scholz, Monika, Sharma, Anuj

[International Worm Meeting, 2019]

How do patterns of neural activity across the brain represent an animal's behavior? Recent techniques for recording from large populations of neurons are providing new insights into how locomotion is encoded in population-level neural activity. Studies from mammalian systems suggest that behavioral information may be more prevalent throughout the brain and may account for a larger fraction of neural dynamics than previously thought. In C. elegans, pioneering studies revealed that the worm's neural dynamics during immobilization exhibit striking stereotyped low-dimensional patterns of neural activity that dominate brain-wide dynamics (Kato et al., 2015). These dynamics are hypothesized to map onto a motor sequence consisting of forward, backward and turning locomotion. One interpretation is that the majority of the worm brain's activity may be involved in encoding these locomotory behaviors. Here we seek to directly measure how patterns of neural activity represent locomotion by recording brain-wide calcium activity in freely-moving animals. We record calcium activity simultaneously from the majority of head neurons in C. elegans during unrestrained spontaneous locomotory behavior (Scholz et al., 2018). We find that a subset of neurons distributed throughout the head encode locomotion. By taking a linear combination of these neurons' activity, we predict the animal's velocity and body curvature and further infer the animal's posture from neural activity alone. The collective activity of these neurons outperforms single neurons at predicting velocity or body curvature. We further attempt to estimate the identity of neurons involved in the prediction. Among neurons important for the prediction are well-known locomotory neurons, as well as neurons not traditionally associated with locomotion. We compare the neural activity of the same animal during unrestrained movement and during immobilization and observe large differences in their neural dynamics. Intriguingly, during unrestrained movement we estimate that only a small fraction of the brain's overall neural dynamics are encoding velocity and body curvature. We speculate that the rest of the brain's neural dynamics may be involved in encoding other behaviors, processing sensory information or maintaining internal brain states. Kato, S., Kaplan, H.S., Schrodel, T., Skora, S., Lindsay, T.H., Yemini, E., Lockery, S., and Zimmer, M. (2015). Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell 163, 656-669. Scholz, M., Linder, A.N., Randi, F., Sharma, A.K., Yu, X., Shaevitz, J.W., and Leifer, A. (2018). Predicting natural behavior from whole-brain neural dynamics. BioRxiv 445643.

Varol, Erdem et al. (2021) International Worm Meeting "A computational approach linking neuron-specific gene expression with connectivity"

Barrett, Alec, McWhirter, Rebecca, Vidal, Berta, Tavazoie, Saeed, Hobert, Oliver, Weinreb, Alexis, Miller, David, Xu, Chuan, Taylor, Seth, Paninski, Liam, Yemini, Eviatar, Sestan, Nenad, Basavaraju, Manasa, Litwin-Kumar, Ashok, Cros, Cyril, Reilly, Molly, Santpere, Gabriel, Poff, Abigail, Glenwinkel, Lori, Abrams, Alexander, Hammarlund, Marc, Rafi, Ibnul, Varol, Erdem, Oikonomou, Panos, Cook, Steven

[International Worm Meeting, 2021]

There is strong prior evidence for genetic encoding of synaptogenesis, axon guidance, and synaptic pruning in neural circuits. Despite these foundational observations, the transcriptional codes that drive neural connectivity have not been elucidated. The C. elegans nervous system is a particularly useful model for studying the interplay between genetics and connectivity since its wiring diagram is highly stereotyped and uniquely well-defined by electron microscopy. Furthermore, recent evidence in C. elegans has suggested that a unique combination of transcription factors specifies each of the 118 neuron classes[1]. Motivated by evidence for the stereotypy of neural circuits and for the genetic encoding of neural identity, we introduce a novel statistical technique, termed Network Differential Gene expression analysis (nDGE), to test the hypotheses that neuron-specific gene expression dictates connectivity. Specifically, we test the hypothesis that pre-synaptic neural identity is defined by a "key" gene combination whose post-synaptic targets are determined by a "lock" gene combination. For our approach, we utilize neuron-specific gene expression profiles from the CeNGEN project[2] to investigate transcriptional codes for connectivity in the nerve ring[3]. We hypothesize that the expression of specific cell adhesion molecules (CAM) among synaptically-connected neurons drives synaptic maintenance in the mature nervous system. We posit that CAMs mediating synaptic stability would be more highly expressed in synaptically-connected neurons than in adjacent neurons with membrane contacts but no synapses. Thus, for each neuron, we compare the expression of all possible combinations of pairs of CAMs in the neuron and its synaptic partners relative to the neuron and its non-synaptic adjacent neurons. Two independent comparisons are generated, one for presynaptic neurons and a second result for postsynaptic neurons. Our nDGE analysis reveals that specific combinations of CAMs are correlated with connectivity in different subsets of neurons and thus provides a uniquely comprehensive road map for investigating the genetic blueprint for the nerve ring wiring diagram. Open source software of Network Differential Gene Expression (nDGE) is publicly available at https://github.com/cengenproject/connectivity_analysis along with a vignette showcasing the CAM results. 1. Reilly, M. B., Cros, C., Varol, E., Yemini, E., & Hobert, O. (2020). Unique homeobox codes delineate all the neuron classes of C. elegans. Nature, 584(7822), 595-601. 2. Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C. Varol, E., ... & Miller, D. M. (2020). Molecular topography of an entire nervous system. bioRxiv. 3. Cook, S. J.,... & Emmons, S. W. (2019). Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature, 571(7763), 63-71.