ABSTRACT
Even in Caenorhabditis elegans, which has only 302 neurons, relationships between behaviors and neural networks are not easily elucidated. In this study, we proposed a novel cellomics approach enabling high-throughput and comprehensive exploration of the functions of a single neuron or a subset of neurons in a complex neural network on a particular behavior. To realize this, we combined optogenetics and Brainbow technologies. Using these technologies, we established a C. elegans library where opsin is labeled in a randomized pattern. Behavioral analysis on this library under light illumination enabled high-throughput annotation of neurons affecting target behaviors. We applied this approach to the egg-laying behavior of C. elegans and succeeded in high-throughput confirmation that hermaphrodite-specific neurons play an important role in the egg-laying behavior. This cellomics approach will lead to the accumulation of neurophysiological and behavioral data of the C. elegans neural network, which is necessary for constructing neuroanatomically grounded models of behavior.
INTRODUCTION
One of the primary objectives of neuroscience is to understand how computations are implemented across neural networks. However, neural networks are so complex that it is not easy to elucidate how they give rise to certain behavior. Hermaphrodites of Caenorhabditis elegans, for instance, have a simple neural network consisting of 302 neurons, and their connectome, a comprehensive map of neural connections, has already been clarified1,2. However, even in C. elegans, the relationships between behaviors and neural networks have not been comprehensively revealed.
Data-driven science, in which an enormous amount of data is accumulated in a hypothesis-free manner, has deepened our understanding of complex biological processes. For example, the applications of state-of-the-art mass spectrometry3 and next-generation sequencers4 have made it easy to acquire omics data on various biological strata, including those concerning genome, transcriptome, proteome, and metabolome, thus streamlining the process of understanding diverse biological processes. However, even with such omics analyses, it is still difficult to gain deeper insights into individual-level biological processes engendered by complex neural networks.
To understand such biological processes derived from complex cellular networks, it is important to develop a new methodology for collecting omics data at the cellular level (cellomics) and implement data-driven science at the individual-level. In recent studies, an approach to reveal cellular network structures comprehensively, which can be called “descriptive” cellomics, has been gradually changing the landscape of neuroscience. Serial electron microscopy5, for example, has been proven to be useful in determining connectomes in various model organisms and promoting comparative connectomics in C. elegans6,7. Moreover, researchers have also developed new approaches, such as Brainbow8 or Optobow9, which involve sparse labeling of fluorescent proteins or opsins in an attempt to reveal brain structures using optical microscopy. Recent improvements in brain visualization10,11 and light-sheet microscopy12 technologies have made it possible to perform high-speed imaging of the mammalian whole brain. These studies have already benefited the field of neuroscience through the accumulation of data on the structure of the brain in a hypothesis-free manner.
To understand the workings of the brain, its structural information is important, but that alone is still insufficient. It is considered essential to accumulate comprehensively neurophysiological and behavioral data on what functions a particular part of the brain has, in an effort to construct neuroanatomically grounded models of behavior. In this context, we considered establishing a novel methodology called “functional” cellomics, which allows comprehensive exploration of what functions a single neuron or a subset of neurons in a complex neural network has on a certain behavior. This sort of approach has already been demonstrated for human brains in the form of natural experiments effectuated by diseases, accidents, wars, and other incidents. Throughout history, loss of function and gain of function screening by natural experiments has caused various serendipitous discoveries, such as the functions of the hippocampus13, which have formed the basis of modern neuroscience. If similar experiments can be performed in a high-throughput and systematic manner at the neural circuit level, they will provide a lot of knowledge in the field of neuroscience.
In this study, we attempted to demonstrate that functional cellomics is feasible using C. elegans and is a promising approach for the comprehensive functional annotation of neural networks. A methodology to realize functional cellomics is required to allow researchers to manipulate a neuron or a subset of neurons in a high-throughput, hypothesis-free, single-cell-resolution, and simple manner and to quantify the effect of such manipulation on a behavior. In C. elegans research, various methodologies for collecting neurophysiological data are available, such as calcium imaging14, optical and electrophysiological recording15, and laser ablation16. However, no single methodology satisfies all of the criteria necessary for realizing the conceptual framework of functional cellomics.
To overcome this obstacle and achieve this approach, we fused optogenetics and Brainbow technologies. Optogenetics is a technique that enables on-demand photoregulation of neural activity through the expression of opsin genes17. In the field of optogenetics, a wide array of tools for activating, suppressing, and killing neurons has been developed and changed the methodology of neuroscience. However, with conventional optogenetics, it is essential to determine in advance which promoters should be used to produce an opsin in specific neurons18. Therefore, this approach is hypothesis-driven, meaning that it is effective in precisely testing existing hypotheses, but is not conducive to establishing entirely new ones (Figure 1a).
To address this issue, we designed a new optogenetic experimental scheme to accomplish hypothesis-free neural network annotation (Figure 1b). Specifically, we attempted to develop a system that stochastically determines whether the effector is produced in each single neuron and to acquire a C. elegans library in which the effector is labeled in diverse patterns. To achieve this stochastic labeling, we adopted Brainbow technologies based on the Cre-lox system8. If a C. elegans library with a random labeling pattern of the effector can be obtained through the use of Brainbow technologies, it would be possible to uncover hitherto unknown relationships between neural networks and behaviors in a high-throughput manner by performing behavioral experiments with the library under light illumination. This approach is hypothesis-free because individuals showing abnormal behavior are first detected and responsible neurons are then identified. By analogy, functional cellomics can be regarded as a cell level application of the forward genetics concept, in which random mutations are introduced across the whole genome and genes altered in mutants showing some phenotypic changes are then identified.
In the present study, we successfully demonstrated the possibility that neurons affecting a behavior of interest can be identified by the combined use of optogenetics and Brainbow technologies. The results substantiated the basic concept of functional cellomics that enables functional annotation of neural networks in a high-throughput, hypothesis-free, single-cell-resolution, and simple manner.
RESULTS
Design of strategy for optogenetic functional cellomics
A methodology to achieve functional cellomics is required to accomplish stochastic labeling of individual neurons with an effector gene in a high-throughput, hypothesis-free, single-cell-resolution, and simple manner. Then, we focused on Brainbow technologies8. Brainbow technologies refer to systems that can stochastically determine whether a certain gene is expressed in a certain cell through the application of the Cre-lox recombination system. In Brainbow technologies, multiple lox variants (e.g., loxP and lox2272 sequences) are inserted alternately downstream of one promoter and two other genes are interposed between these lox sequences (Figure 2). If Cre recombinase is allowed to act on this sequence, excision occurs exclusively either between loxP sequences or between lox2272 sequences. Consequently, it becomes possible to determine which of these two genes is expressed in a Cre-dependent manner.
To implement functional cellomics, we designed four plasmids (Figure 2a). The plasmid pCre expresses Cre recombinase in response to a heat shock. In the plasmid pSTAR, lox sequences, mCherry, and a transcription factor (QF2w19) were inserted downstream of a pan-neuronal promoter (F25B3.3p). The plasmid pQUAS_ChR2_GFP expresses an arbitrary effector gene in a QF2w-dependent manner. In the present study, we adopted channelrhodopsin-2 fused with GFP (ChR2::GFP). Since the constructs producing a transcription factor or an effector are modularized, it is easy to use not only opsin but also various other effectors. We also constructed pF25B3.3p_mCherry, which continuously expresses mCherry even after Cre recombination.
When all of these plasmids are introduced into C. elegans, all neurons produce only mCherry at the initial state. After a heat shock is applied to induce Cre recombinase, the production of mCherry continues if the genome is excised between loxP sequences, while QF2w is instead produced if an excision occurs between lox2272 sequences. In neurons producing QF2w, ChR2-GFP is produced as an effector, enabling the on-demand activation of these neurons by light illumination. Since GFP is fused to ChR2, it is easy to identify which neurons are producing opsin following a behavioral experiment.
Stochastic labeling of neurons at single-cell level
We introduced the above-mentioned four plasmids into C. elegans to establish the AYK338 strain (aykEx338 [hsp-16.2p::Cre, F25B3.3p::lox2272::mCherry::loxP::lox2272::QF2w::loxP, QUAS::ChR2::GFP, F25B3.3p:: mCherry]). After this C. elegans strain had propagated, a brief heat shock was applied to determine whether ChR2-GFP was labeled stochastically in each C. elegans individual. After isolating three individuals, we observed their mid-body sections where the neuron density was low at a magnification of 40×. The results revealed that the labeling pattern of ChR2-GFP differed from one individual to another (Figure 2b).
Identification of neurons responsible for egg-laying behavior
In functional cellomics, stochastic labeling of an effector gene makes it possible to explore the relationships between neural networks and behaviors in a hypothesis-free and comprehensive manner. To demonstrate the feasibility of functional cellomics, we selected the egg-laying behavior of C. elegans as a model. It is known that a relatively simple neural network is responsible for controlling the egg-laying behavior of this nematode. In this behavior, two hermaphrodite-specific neurons (HSNs: HSNR and HSNL) play a central role through direct excitation of vulval muscles and ventral C neurons (VCs)20–22. Moreover, it is known that the activation of the HSNs by ChR2 induces egg-laying behavior23. If the HSNs can be identified by functional cellomics in a high-throughput manner, it shows that this strategy actually works.
We constructed a C. elegans library in which ChR2-GFP is stochastically labeled. Individuals of this transgenic C. elegans were filmed for 30 sec while illuminated with blue light (Figure 3a). Among the filmed individuals, 65% laid eggs in a light-dependent manner, whereas 35% did not (Figure 3bc). When a similar experiment was conducted without all-trans retinal (ATR), a cofactor of ChR2, no egg-laying behavior was observed. These results indicate that the egg-laying behavior observed in this experiment is ChR2-dependent, and that individual nematodes exhibiting the target phenotype can be readily obtained through the stochastic labeling of an effector.
Next, we used confocal laser scanning microscopy to determine whether ChR2-GFP was produced in the HSNs. After isolating egg-laying and non-egg-laying individuals and observing the vicinity of the vulva, we confirmed that GhR2-GFP was produced in the HSNs in the egg-laying individuals (Figure 4a), but it was not in the non-egg-laying individuals (Figure 4b). In the representative individual shown in Figure 4a, the production of ChR2-GFP was seen in HSNR but not in HSNL. A previous study demonstrated that killing one HSN by laser ablation did not markedly affect the egg-laying behavior of the nematode, whereas killing both HSNs resulted in strong inhibition of the egg-laying behavior21. Our result that the egg-laying behavior was induced sufficiently by activating only one HSN is consistent with that of this previous study.
DISCUSSION
Although various methodologies have been established to explore the properties of neural networks, no single methodology satisfies all of the criteria necessary for realizing the conceptual framework of functional cellomics. One typical example of existing methods, which is very easy yet effective, is to induce the production of effectors using cell-type-specific promoters. However, this approach is basically hypothesis-driven because one needs to select specific promoters a priori, meaning that it is not suitable for establishing entirely new hypotheses. Moreover, it is also difficult to analyze neural networks at single-cell resolution because C. elegans has few single-cell-specific promoters. By extension, data-driven neuron-behavior mapping is not impracticable with this approach. In Drosophila melanogaster, neuron-behavior mapping was attempted using 1049 distinct cell-type-specific GAL4 lines to selectively target ChR224. As a result, 29 discrete, statistically distinguishable behavioral phenotypes were discovered, indicating that data-driven neuron-behavior mapping is a useful approach. However, owing to the fact that a large number of D. melanogaster lines are required, this method is costly and laborious. Furthermore, as this method utilizes cell-type-specific promoters, neurons cannot be manipulated at single-cell resolution. Another example is laser ablation, which enables hypothesis-free and single-cell-resolution analysis16. Although this is a powerful technique applicable to any species, its low throughput makes it difficult to conduct experiments involving various patterns of intervention. Additionally, laser ablation lacks expandability in that it cannot activate or suppress neurons. A recent study has suggested the possibility that contradictory results are generated depending on the mode of intervention25, indicating the need to compare results obtained from various modes of intervention (activation, suppression, killing, etc.). Another method enabling analysis at single-cell resolution involves application of a heat shock to only specific cells to induce effectors26,27. This method is capable of manipulating neurons in many ways; however, similar to laser ablation, it is also restricted by its low throughput capacity. The patterned illumination technique using digital micromirror devices, whose development has been advancing in recent years, allows for a high degree of freedom in experimental design and has a relatively high throughput23. However, it is still difficult to perform accurate analysis at single-cell resolution with this method because multiple adjacent neurons may be illuminated simultaneously, unless sufficiently sparse expression patterns of effectors are provided28.
Functional cellomics described in this study is the first approach to combine all properties necessary for achieving individual-level cellomics, that is, high-throughput, hypothesis-free, single-cell resolution, and simplicity. In fact, by applying functional cellomics to the egg-laying behavior of C. elegans, we managed to establish the proof of concept of this approach.
Compared with the existing methodologies, this system has advantages in terms of throughput, resolution, and expandability. First, it can be easily implemented in any laboratory without requiring any specialized equipment. Second, having no limitations in feasible labeling patterns, it is completely hypothesis-free, facilitating easy labeling at singlecell resolution, even for bilaterally symmetrical neuron pairs with almost identical gene expression patterns. Third, with one transgenic C. elegans individual capable of propagating many other individuals with different labeling patterns, it is both simple and high-throughput. Fourth, it can intervene in neural networks in various fashions. Besides opsin, which was employed in the present study, any effectors can be used as long as they cause either loss of function or gain of function in neurons. This enables a variety of interventions, such as cell killing29,30, suppression31, activation32, and gene expression control33,34. Fifth, multitudes of experimental designs are available using C. elegans promoterome35 and bipartite gene expression systems such as QF2w and Gal436. For example, instead of a pan-neuronal promoter, one can use a promoter specific to a subset of neurons for more “focused” functional cellomics. In addition, simultaneously employing QF2w and Gal4, one can label multiple effectors in a stochastic fashion.
Although promising, there are two points of concern regarding functional cellomics. One is the high probability of effector labeling, which makes it difficult to calculate the labeling rate accurately. To perform a well-designed experiment, it is necessary to achieve strict control of the probability of effector labeling, as is the case with forward genetics in which the mutation rate is predetermined. Figure 3c shows that a large population of C. elegans (65%) displayed egg-laying behavior, and this was probably because a large proportion of neurons were labeled by opsin. When too many neurons are labeled by effectors, it becomes difficult to determine which neurons are responsible for the target behavior. Concomitant use of single-copy integration and lox variants may be able to control the probability of labeling only a desired number of all 302 neurons of a C. elegans hermaphrodite. In our laboratory, we are proceeding with the construction of a more sophisticated system that will help improve this issue.
The second point is how to ensure that the obtained results are reproducible. If functional cellomics implies that a certain labeling pattern may affect the target behavior, it is still necessary to verify this by other methods. To reproduce the labeling pattern, methodologies that can evoke gene expression in arbitrary cells, such as use of a pulsed infrared laser26 and multi-step optogenetics27, may be applied to verify the results relatively easily. Besides, the intersectional Cre-lox strategy37 and multiple-feature Boolean logic38 may also be applicable to reproducing the labeling pattern.
In conclusion, we have demonstrated for the first time the possibility of identifying neurons responsible for a target behavior by randomizing the labeling patterns of effector genes based on Brainbow technologies. The results substantiated the basic concept of functional cellomics, which enables functional annotation of neural networks of C. elegans in a high-throughput, hypothesis-free, single-cell-resolution, and simple manner. Since its connectome information is already mapped and available, C. elegans is highly compatible with functional cellomics. By overlaying the connectome information with the results of cyclopedic intervention experiments by functional cellomics, we should become able to construct neuroanatomically grounded models of behavior that can explain how complex neural networks implement computation.
METHODS
Construction of plasmids
To construct pCre, SV40NLS-Cre was amplified from pPGK-Cre-bpA deposited by Klaus Rajewsky (Addgene plasmid #11543). The amplified fragment was inserted into pPD49_78 deposited by Andrew Fire (Addgene plasmid #1447).
To construct pSTAR, a backbone plasmid with lox and QF2w19 sequences was synthesized (Thermo Fisher Scientific, MA, USA). In addition, mCherry was subcloned from pGH839 deposited by Erik Jorgensen (Addgene plasmid #19359) and a pan-neuronal promoter, F25B3.3p, was cloned from the C. elegans genome. These two fragments were inserted into the backbone plasmid.
To construct pQUAS_ChR2_GFP, ChR2 (H134R) and GFP (S65C) were amplified from pAAV-Ef1a-vCreDIO hChR2(H134R)-EYFP deposited by Karl Deisseroth (Addgene plasmid #55643) and L2680 deposited by Andrew Fire (Addgene plasmid #1516), respectively. In addition, a QF2w-dependent promoter sequence, QUAS::Δpes-1040, was synthesized (Thermo Fisher Scientific). These three fragments were inserted into pPD49_78.
To construct pF25B3.3p_mCherry, F25B3.3p and mCherry were subcloned from pSTAR. These fragments were inserted into pPD49_78.
Culture conditions
Worms were cultivated on nematode growth medium (NGM) plates with Escherichia coli OP50. Specifically, the OP50 plates were prepared with 250 μL of OP50 seeded into 6cm NGM plates. The worms were maintained at 20°C, with care taken to ensure that the temperature shifted as little as possible. To perform optogenetic experiments, 300 μL of 500 μM ATR; Sigma-Aldrich, MO, USA) was added to solid NGM plates with E. coli, and the samples were allowed to dry while shielded from light by aluminum foil.
Transgenic strains
Injections into the nematodes were performed with the aid of a stereomicroscope (SZX10; Olympus, Tokyo, Japan) equipped with a Femtojet 4i (5252 000.021; Eppendorf, Hamburg, Germany) and Femtotips II (1501040; Eppendorf). The strain AYK338 (aykEx338 [hsp-16.2p::Cre, F25B3.3p::lox2272::mCherry::loxP::lox2272::QF2w::loxP, QUAS::ChR2::GFP, F25B3.3p:: mCherry]) was generated by co-injecting the four plasmids constructed in this study (50 ng μL-1 each in water) into the C. elegans N2 background. The injection was performed on 20 nematodes with an N2 background, and three mCherry-expressing lines were obtained.
Induction of Cre recombinase for stochastic labeling of ChR2
Transgenic worms were placed on NGM plates with or without ATR. The worms were incubated at 37°C for 30 min for the induction of Cre recombinase by heat shock, after which they were placed in an incubator at 20°C. At 12 h after the heat shock, the worms were examined by egg-laying assay and/or fluorescence microscopy.
Fluorescence microscopy
A 5% agarose pad (01149-05; Nacalai Tesque, Kyoto, Japan) was prepared, onto which 5 μL of 50 mM sodium azide (830011; Nacalai Tesque) was placed. C. elegans worms were picked up and placed onto the agarose pad with sodium azide, over which a cover glass was placed gently. Fluorescence was observed by confocal laser scanning microscopy (LSM700; Carl Zeiss, Oberkochen, Germany). Fluorescence of GFP and mCherry was observed using 488 nm and 561 nm lasers, respectively. Acquired images were processed using Zen Lite, Imaris, or ImageJ41 software.
Egg-laying assay
The worms were observed under a stereomicroscope (SZX10; Olympus) equipped with a camera (HAS-L1; DITECT, Tokyo, Japan). The SZX10’s halogen lamp (410849; PHILIPS, Amsterdam, the Netherlands) was fitted with an optical filter (Asahi Spectra, Tokyo, Japan) that blocks wavelengths below 600 nm to prevent ChR2-GFP from being activated within the worms during observation. To activate ChR2-GFP, the worms were illuminated using blue light (LDL2-98X30BL2; CCS, Kyoto, Japan) powered by PD3-5024-4-PI (CCS). To prevent the LED’s blue light from being detected by the camera, the object lens was fitted with an optical filter (Asahi Spectra) to block wavelengths below 570 nm.
For an egg-laying assay to examine light-dependent behavior modulation, each worm was transferred to a 6-cm agar NGM plate without E. coli and filmed for 30 sec. During this filming, the blue light was turned on and off at 5-second intervals, and the individuals that exhibited egg-laying behavior during 30 sec were defined as egg-laying individuals. Irrespective of the absence or presence of E. coli, the activation of the HSNs induces egglaying behavior23.
AUTHOR CONTRIBUTIONS
W.A. conceived the project. W.A., H.M., Y.Y., and H.Y. performed experiments and data analysis. K.T., M.M., K.H., R.S., and M.U. provided advice on method development. The manuscript was prepared by W.A. and edited by all co-authors.
COMPETING FINANCIAL INTERESTS STATEMENT
The authors have no competing financial interests to declare.
ACKNOWLEDGMENTS
We thank Kenta Terai and Michiyuki Matsuda for valuable advice on fluorescence imaging. We thank Crimson Interactive Pvt. Ltd. for their assistance with manuscript editing. This work was supported by PREST, JST (grant No. JPMJPR16F1), JSPS KAKENHI (grant No. JP17K19452), and Kyoto University Live Imaging Center.