ST), haplotype inference, and whole chromosome scanning for signals of positive or of balancing selection. Derived alleles were determined by comparison to archaic hominin and other primate genomes. The potential effect of common non-synonymous variants on protein-protein interaction was also assessed. IZUMO1 displays higher variability among human individuals than JUNO. Genetic differentiation between continental population pairs was within whole-genome estimates for all but the JUNO gene in the African population group with respect to the other 4 population groups . Tajima’s D values demonstrated deviation from neutrality for both genes in comparison to a group of genes identified in the literature as under balancing or positive selection. Tajima’s D for IZUMO1 aligns with values calculated for genes presumed to be under balancing selection, whereas JUNO’s value aligned with genes presumed to be under positive selection. These inferences on selection are both supported by SNP density, nucleotide diversity and haplotype analysis. A JUNO haplotype carrying 3 derived alleles out of 5, one of which is a missense mutation implicated in polyspermy, was found to be significant in a population of African ancestry. Polyspermy has a disadvantageous impact on fertility and its presence in approximately 30% of the population of African ancestry may be associated to a potentially beneficial role of this haplotype. This role has not been established and may be related to a non-reproductive role of JUNO. The high degree of conservation of the JUNO sequence combined with a dominant haplotype across multiple population groups supports JUNO as a potential target for the development of contraceptive treatments. In addition to providing a detailed account of human genetic diversity across these 2 important and related genes, this study also provides a framework for large population-based studies investigating protein-protein interactions at the genome level.Fertilization in mammals begins with the union of egg and sperm, an event that starts a cascade of cellular processes. The molecular-level understanding of these processes can guide the development of new strategies for controlling and/or promoting fertilization, and inform researchers and medical professional on the best choice of interventions. The proteins encoded by the IZUMO1 and JUNO genes form a ligand-receptor protein pair involved in the recognition of sperm and egg. Due to their role in the fertilization process, these proteins are potential targets for the development of novel anti-contraceptive, as well as infertility treatments. Here we present a comprehensive analysis of these gene sequences, with the objective of identifying evolutionary patterns that may support their relevance as targets for preventing or improving fertility among humans. JUNO and IZUMO1 gene sequences were identified within the genomes of over 2,000 humans sequenced in the 1000 Genomes Project. The human sequences were subjected to analyses of nucleotide diversity, deviation from neutrality of genetic variation, population-based differentiation (F Fertilization is a multistep process that includes sperm-egg recognition and sperm-egg fusion, resulting in a genetically distinct diploid organism . Fusion IZUMO1 is a protein located on the cell surface of capacitated sperm cells, named after a Japanese marriage shrine . It is eDue to their participation in the early steps of fertilization, it has been suggested that IZUMO1 and JUNO could play a significant role in infertility , 11. TheIn this work, the genetic sequences of both IZUMO1 and JUNO protein coding genes in 2,504 humans sequenced in the 1000 Genomes project  were retST for selected genes. Our comparisons of Tajima’s D, nucleotide diversity, and overall FST thus correspond to different genes present on the same individuals used in the IZUMO1 and JUNO analyzes. In addition, scanning for signals of selection was performed on the complete sequences of human chromosomes 11 and 19 from the 1000 Genomes project and it is, thus, based on the same samples as the other analyzes. To our knowledge, the 1000 Genomes dataset is one of the most comprehensive large population-based whole-genome dataset publicly available.All the analyses presented for human IZUMO1 and JUNO in this manuscript were based on whole-genome sequence data from the phase 3 of the 1000 Genomes project. The 1000 Genomes project dataset corresponds to full-length DNA sequences of 5,008 chromatids from 2504 human individuals belonging to 26 population groups clustered into 5 larger population groups (African (AFR), American (AMR), East Asian (EAS), European (EUR) and South Asian (SAS)). The number of individuals in each population group as well as the composition of the larger groups in terms of the 26 populations are described in The IZUMO1 and JUNO genetic sequences of 2504 individuals included in the 1000 Genomes project  were obtSNV density (number of SNVs per 100 bp) in African and non-African population groups for IZUMO1 and JUNO is compared to other genes in Single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) ≥1% or ≥5% are distinguished from total SNVs for each group of genes. SNV density is generally higher in non-African populations. This difference can be attributed to a larger number of rare alleles (MAF < 1%) in non-Africans, as indicated by the larger gap between total number of SNVs and number of SNPs at 1% and 5% MAF cutoffs. JUNO in non-Africans has a larger number of rare alleles than IZUMO1. In contrast, IZUMO1 has larger percent of variant sites with a MAF ≥ 1% in both African (56%) and non-African populations (28%) than in the JUNO gene (19% in African and 9% in non-African populations), indicating that JUNO is more conserved across humans than IZUMO1. Neither JUNO or IZUMO1 stand out from the other genes selected for comparison. Genes classified in the literature as under balancing selection trend, on average, towards higher SNV density than genes believed to be under positive selection.In SNPEff  indicateTajima’s D is a null hypothesis test of deviations from neutrality. Significant deviation from zero indicates that the null hypothesis of neutrality is rejected, and that a non-random process is driving the evolution of the sequence . GeneralNucleotide diversity (π) was estimated for the same genes and populations used for Tajima’s D as an additional characterization of selection . NucleotHaploview  was usedA haplotype analysis was carried out for IZUMO1 and JUNO using the variant sites passing MAF and HWE thresholds. The individual sequences were grouped according to sex , and larger groups  of the original 26 populations. In addition, population analysis was performed using sequences from only females for the JUNO gene and from only males for the IZUMO1 gene, since the role of these proteins in reproduction can only be fully understood in the gametes of their respective sex. The data collected from this analysis is presented in Tables Analysis of linkage disequilibrium can be heavily influenced by sample size and it is generally believed that relatively large samples are required for detection of disequilibrium –41. How The haplotypes reported all contain missense mutations, which are underlined in the haplotypes in Tables AGA. A secondary high-frequency haplotype is present in the AMR and SAS population groups at the same frequency, 29%, but with different allele combinations . Interestingly, EAS presents overwhelmingly as AGA (83%), whereas SAS is 54% AGA and 29% GGC indicating that these two populations do not form a homogeneous group together (they are often grouped as “Asian” in the literature). The haplotypes AGA (45%) and GGC (46%) are evenly distributed in the EUR population group. The presence of multiple haplotypes at considerable frequencies in IZUMO1 is consistent with balancing selection. Two of the three variant positions that are part of the haplotypes for IZUMO1 are found in NCBI’s SNP database (dbSNP) [For IZUMO1, there are 3 haplotypes that appear equally frequently in the AFR population, with no sex difference. Differences between 1% and 2% in the haplotype frequency of males only compared to both sexes and females only are seen in the other populations. However, these differences could be attributed to incorrect haplogroup assignment due to error in variant calling, which is estimated to be between 0.1% and 1%, incorrectly , 43. EAS (dbSNP) , however (dbSNP) . In the  (dbSNP) . This isypes AAG % and CGGCGTTG) with frequency at or above 97%, except AFR. A 9% sex bias is seen in AFR for this haplotype. Sex bias is not observed in any other population group. AFR shows more diversity than the other population groups with the second haplotype present in 26% of the population. A third haplotype that is only present in the AFR population was identified but at a low frequency . This haplotype is not observed in any other population group. This further supports the conclusion that the largest difference in the JUNO gene is seen in the AFR population. Both the r2 values and the D’ values for the JUNO haplotypes are high, indicating strong linkage between all the loci involved in the haplotypes. The dominance of one haplotype in JUNO is consistent with both neutral evolution and positive selection, which may be distinguished from one another by determining the ancestral alleles which we will discuss in later sections. All five positions in the haplotype appear in dbSNP [in-vitro fertilization [GACGA, is significantly present in the AFR population (26% frequency) and, interestingly, displays a sex bias with a lower prevalence in females (17%) compared to the group as a whole (26%). The lower frequency of this haplotype in AFR females may reflect impaired fertility in females carrying this mutation. The frequency of this allele in AFR is much higher than in the other populations, suggesting the existence of a genetic advantage that has yet to be identified for the carriers of the G allele. Further investigation of the role of JUNO in biological processes other than reproduction may provide insights on the evolutionary role of the GACGA JUNO haplotype.For JUNO, all population groups present one dominant haplotype . To test for indirect co-dependency between the 2 genes, we calculated the frequencies at which each JUNO haplotype is observed in combination to each IZUMO1 haplotype within the same chromatid. This data is shown in AG-CGTTGA over AG-GACGAA, likely due to the female bias found for CGTTG in AFR. However this difference is not significant, as determined by Wilcoxon matched-pairs test yielding a two-tailed p-value of 0.99. Overall, we find no indication of strong genetic linkage between the two genes, since their combined haplotypes are observed at the same frequencies they are expected to be observed by chance.IZUMO1 and JUNO are a receptor-ligand pair encoded in 2 autosome chromosomes. Due to their role in fertilization, their expression and function is well-documented in gametes but not in other cell types. IZUMO1 is overwhelmingly expressed in testes, with low levels of expression in lungs, stomach, and adrenal glands, among other organs , 48. JUN10 helices, and two short two-stranded antiparallel beta sheets, stabilized by eight conserved disulfide bonds [The structures of the IZUMO1 and JUNO proteins, as well as their complex have been determined experimentally –52. On ide bonds –52. JUNOThese proteins bind with high affinity in a one-to-one ratio via the N-terminal of IZUMO1, resulting in major conformational changes observed in the four-helix bundle (4HB) and hinge region of IZUMO1. IZUMO1 abandons its boomerang shape and adopts a stabilized and locked upright conformation  upon binNeither one of the non-synonymous variant positions that are part of our haplotypes for IZUMO1 and JUNO is found in the experimentally determined structures. The experimentally determined structures of IZUMO1 normally span from amino acids 22 to 255 (out of 355 amino acids), which represent the extracellular regions of the protein. The non-synonymous SNP in our haplotype (rs2307019) encodes for position 333 of the cytoplasmic domain of IZUMO1 protein, which is located outside the boundaries of the currently available 3D structures. This position it is not located near the interaction surface of these two proteins (residues 57–113)  and, theThe currently available experimentally determined structures of JUNO span from amino acid 20 to 228, encompassing the majority of the 250 amino acids of this protein. The SNP of interest in our haplotype (rs61742524) is part of the codon position 3 of the JUNO protein which is not included in any of the currently available experimentally determined structures. The fact that the missense mutation (Cys3Trp) occurs far away from the interaction surface of the two proteins (roughly from Lys42 to Tyr 147) . JUNO ishttp://genetics.bwh.harvard.edu/pph2/) [Due to the lack of experimentally determined structures with the mutations highlighted in this work  an additional approach was used to assess the effect of these substitutions. PolyPhen-2, a human nonsynonymous SNP prediction database  , was empu/pph2/) . The Alau/pph2/) .ST) values between different populations were calculated. FST is a comparative measure of the average number of pairwise differences between individuals sampled from different population groups and the average number of pairwise differences between individuals sampled from the same population group [ST value of 0.124. This number is an average of 3 human genome wide FST values reported in the literature. The first (0.123) was calculated for the entire human genome using 26,530 autosomal SNPs from 3 different populations  [ST value (0.119) was calculated by Cavalli et al. using 120 “classical” non-DNA polymorphisms in 42 worldwide populations [ST value of 0.130 [ST for the set of reference genes used in the Tajima’s D and nucleotide diversity analysis, and found the mean of these values (0.103 ± 0.063) to be consistent with the genome-wide literature-based value but carrying a much larger standard deviation. Thus, an FST value for the IZUMO1 and JUNO genetic sequences of the different population pairs that is equal or lower than 0.124 plus or minus two standard deviations is deemed to be consistent with genome wide FST values and in line with the amount of variation seen within the entire human genome. The pairwise FST values for each pair of population groups for IZUMO1 and for JUNO are shown in ST values between all populations are displayed in ST of 0.291 ± 0112. However, this divergence is short of statistical significance , indicating that the differentiation seen in this gene between all four continental populations and EAS is still in line with the differentiation seen within the entire human genome. The average FST value (0.266 ± 0.051) between all four continental populations for JUNO with respect to AFR is significantly higher than the genome wide FST average , indicating a significant degree of population divergence. This divergence likely results from a 26% prevalence of the JUNO haplotype GACGA in the AFR population, but only 3% or less prevalence of this haplotype in the EUR, SAS, AMR, and EAS population groups. The average FST values for EUR, SAS, AMR, and EAS population groups in the JUNO gene are in line with the benchmark genome-wide value.The Weir-Cockerham fixation index (Fon group . We selemerican) . The seculations . A thirdof 0.130 . These vST values of population groups for all individuals, and for just males or just females with respect to EAS for IZUMO1 and to AFR for JUNO are presented in ST values in the data sets.The pairwise FST between the males and the females of all four of the continental populations was 0.0008 for IZUMO1 and 0.0007 for JUNO, respectively. Both are close to zero, indicating that there are no significant differences between the female and male sequences of IZUMO1 and JUNO. These FST values were calculated using SNPs with a MAF of 1%.FST values associated to all 26 original populations that form the 5 continental groups EAS, EUR, SAS, AMR and AFR, considering only SNPs with a MAF of 5% when all individuals from all populations are grouped together. We used these values in a principal component analysis (PCA) to map genetic diversity between the 26 population groups represented in the 1000 Genomes dataset  cluster together and are separate from the other core populations for both IZUMO1 and JUNO. Populations belonging to the EAS group (circles) are separate from the others for IZUMO1, but overlap with populations belonging to the EUR (crosses) and SAS (stars) groups for JUNO. Similarly, core geographic populations in the AMR group (triangles) are separate for JUNO but overlap with other geographic groups for IZUMO1. For IZUMO1, the core populations GBR and CEU segregate from the other EUR groups and do not overlap with any other core populations. Similarly, the core population LWK (AFR) segregates from the other AFR core populations for JUNO. These results do not support a model of co-selection between IZUMO1 and JUNO, as the pattern of genetic diversity represented by the PCs is not as similar as it would have been expected if these 2 genes were co-selected.Homo were downloaded from the Max Planck Institute for Evolutionary Anthropology  in vcf format, and analyzed to identify alleles at locations corresponding to haplotypes for IZUMO1 (Genomes of archaic members of the genus r IZUMO1  and JUNOr IZUMO1  in conteGC. A C at rs2307018 and G at rs2307019 correspond to either a GAC or a GGC haplotype in GGC haplotype is most prominent in EUR (0.46). GAC is present at moderate frequencies in AFR (0.35) and SAS (0.29). Assuming the same pattern of heritance, the heterozygous Ust’-Ishim likely had one AGA parent. This haplotype is predominant in EAS (0.83) and AMR (0.54), and is prominently present in EUR (0.45).Two out of 3 DNA markers for IZUMO1 (rs2307018 and rs2307019) are present in the genome sequences of the Neanderthal from the Vindija Cave  and the CGTTG, which is the major haplotype in present-day humans analyzed in this report. This haplotype has a frequency greater than 0.97 in every population except the AFR population (0.69). This could indicate that the JUNO gene has diverged from its ancient genetic sequence through selective pressures in AFR, or that inter-breeding between modern humans, Neanderthal and Denisova brought this haplotype to higher frequencies in all but AFR populations. As we shall see in the next section, the later is the most likely to have happened.JUNO’s DNA markers rs61742524, rs55784852, rs16920146, rs7925833 and rs7935583 were present in the genomes of the Vindija cave Neanderthal , Altai NGGC in our analysis which is 1 of 2 equally represented haplotypes in EUR (0.46 frequency) and it is 1 of 3 equally represented haplotypes in AFR (0.33 frequency). The dominant haplotype in EAS, AGA (0.83 frequency), contains 2 derived alleles out of 3, whereas the third IZUMO1 haplotype (GAC) contains one derived allele at the last position. The AGA haplotype is well-represented in all 5 populations, albeit it is only dominant in EAS, and may have resulted from a founder effect or a selective sweep in this population. For JUNO, the ancient alleles at positions rs61742524, rs55784852, rs16920146, rs7925833 and rs7935583 are GGCTG, in this order. This allele combination does not correspond to either one of our haplotypes. The haplotype (CGTTG) that we identified as prevalent in all of the 5 populations analyzed here contain 2 derived alleles, whereas the second JUNO haplotype (GACGA), which is mostly present in AFR populations, contains 3 derived alleles. The existence of multiple high frequency derived alleles within a relatively small genome segment is consistent with positive selection [A pre-built genomic alignment of 37 mammals  was usedHomo sapiens IZUMO1 and JUNO amino acid sequences were used in this study. These sequences were aligned with ClustalX [Homo sapiens amino acid sequences for both JUNO and IZUMO1 can be found in There are many homologous mammalian species for IZUMO1 and JUNO protein sequences. However, only 29 mammalian species with available matches to both the ClustalX . The perThe nucleotide sequences of all the 2504 individuals from the 1000 Genomes Project for JUNO and IZUMO1 were aligned using ClustalX . The perWe used RAiSD  to scan As an independent verification that IZUMO1 is under balancing selection, we scanned chromosome 19 (where IZUMO1 is located) for signals of balancing selection using BetaScan2 . This coST values below a genome-wide FST average obtained from multiple studies. However, there were some pairwise FST values that indicated greater diversity between populations, such as all other continental populations with respect to EAS in the IZUMO1 gene, and with respect to AFR in the JUNO gene. The trend associated to the African population group in the JUNO gene is consistent with positive selection which, as we will discuss later, is supported by other metrics.The IZUMO1 and JUNO genes encode 2 protein partners that play an essential role in fertilization. In this manuscript we present the first large population genetics study on the IZUMO1 and JUNO genes, with some very interesting results. It was expected that the IZUMO1 and JUNO genes would be well-conserved between human populations, given their essential role in fertilization. In general, this held true with both genes demonstrating pairwise-FGACGA, that has very low frequency in all other populations has a frequency almost ten-fold higher in the AFR population, suggesting that a selective advantage that has yet to be identified is carried by this haplotype. All 3 archaic genomes we examined have the haplotype CGTTG, which is the major haplotype in present-day humans, and primates carry the C allele in the first position of this haplotype. This indicates that the G allele at locus rs61742524 is a derived allele. Since the G allele is associated to polyspermy, which is disadvantageous for female reproduction, its relatively high frequency in AFR is likely associated to a non-reproductive role of JUNO. Investigating the role of the JUNO protein in non-reproductive cells may, thus, shed light on functional effects associated to the rs61742524 G allele. We found no LD between intergenic alleles and no significant differences between observed and predicted combined IZUMO1-JUNO haplotype frequencies indicating that the two genes are not genetically linked within the same genome. Thus, cross-genes haplotype analysis failed to provide evidence of co-selection for this pair of interacting genes, and so did PCA genetic diversity mapping.Interestingly, a JUNO haplotype, Metrics such as SNV density, nucleotide diversity (π) and Tajima’s D, as well as haplotype analysis and derived allele determination, all support the notion that the IZUMO1 gene in humans is under balancing selection whereas the JUNO gene is under positive selection. These assertions are further supported by chromosome-level scanning for signals of positive and balancing selection on chromosomes 11 and 19, respectively. The indication that IZUMO1 is under balancing selection seems to contradict that in mammals, many male reproductive genes have been shown to evolve rapidly through positive selection –70 On thRegarding the impact of genetic diversity on fertility, a study using recombinant proteins showed that hamster JUNO can directly interact with mouse, pig and human IZUMO1, matching the ability of zona-free hamster eggs to fuse with human, mouse and pig spermatozoa . This inEven though these genes code proteins that directly interact in a key step of fertilization. A great deal of tolerance in diversity of the IZUMO1 sequence, matched by a great deal of conservation of the JUNO sequence, seems to be sufficient to establish some degree of genetic “checks and balance” for this pair. The high degree of conservation combined to a dominant haplotype across multiple population groups makes the protein encoded by JUNO a potential target for the development of contraceptive treatments (with the caveat that such treatments would have to pass the zona pellucida barrier). These contraceptive treatments could include small molecules designed to bind to the JUNO protein in the region of the binding interactions to the IZUMO1 protein, delivered systemically or locally.Currently, there is a demand for population-based analysis workflows due to the increasing availability of full genome sequences. This demand is compounded by the realization that, in order to fully understand how genomes work, we must extend traditional population analysis to an interacting network of genes context. This study attends that demand by providing a framework for comprehensive analysis of multiple interacting genes.ST) to investigate the genetic diversity seen in the IZUMO1 and JUNO genes between human populations [http://wesley.lakeheadu.ca:8800), which is only accessible within the University’s network, or through in-house developed R [There are a wide variety of population genetic approaches to analyze human genes, however, in this work, only a few were selected to best answer the question of how inter-dependent the IZUMO1 and JUNO are among human populations. A method based on population allele frequency differences was selected  or balaulations ) selectieloped R  Scripts https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes). We obtained vcf files for IZUMO1 and JUNO as well as 40 selected genes used as reference, and for chromosomes 11 and 19. All of these are from the same set of 2504 individuals whose genomes were fully sequenced by the 1000 Genomes Project. The genomic locations (chromosome:first-last genomic coordinate) for IZUMO1 and JUNO were, respectively, 19:49244073–49250166 and 11:94038803–94040858. The genomic locations of the other genes are reported in https://vcftools.github.io/index.html), a table containing all of the single nucleotide polymorphisms (SNPs) for each gene was produced and allele frequencies for all sites in each gene were calculated.Data from phase 3 of the 1000 Genome Project  was retrhttp://blast.ncbi.nlm.nih.gov/Blast.cgi) [Blast (ast.cgi)  was usedhttps://galaxyproject.org) was used to determine the number of SNPs and their effects. This application determines the number of SNPs, where the SNPs are located and the effects of the SNPs [The program SNPeff  implementhe SNPs , 38. Thethe SNPs . It shouFor population-based analysis, we used the original 1000 Genomes 26 populations, as well as “supergroups” of the original populations . These sST values between each of the regional populations, as well as each of the supergroup populations, were estimated after excluding rare alleles (MAF < 1%), as their inclusion may introduce ascertainment bias if the distribution of rare alleles is uneven across the individual populations [ST values were calculated using the R package HierFSTAT (https://github.com/jgx65/hierfstat). For PCA of FST values, we used the R package FactoMineR [http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_sample_info.xlsx. Supergroup membership was assigned according to ST values calculated between regional population pairs or their supergroups .An R script was developed to calculate population genetics parameters and perform Principal Components Analysis. The script reads vcf files as input and converts the data to the various formats required by individual R packages. The input vcf files are pre-filtered using vcftools  to elimiulations , which sulations . LD and ulations  was usedulations  FST valuctoMineR . GeograpThe program Haploview  was usedhttps://vcftools.github.io/index.html) to calculate r2 between pairs of SNPs where each SNP in the pair came from a different gene. We only considered SNPs with a MAF of 1% or greater. This set, thus, included all SNPs used in Haplotype inference of each gene.We used vcftools  (https:/http://genetics.bwh.harvard.edu/pph2/) [The PolyPhen-2, a human nonsynonymous SNP prediction database  , was empu/pph2/) . The amihttp://cdna.eva.mpg.de/neandertal/altai/) in vcf format. The segments corresponding to the IZUMO1 and JUNO genes were isolated using vcftools [The ancient genomes of Denisova, Vindija Neanderthal, Altai Neanderthal and Ust’-Ishim ancient modern human were downloaded from the Max Planck Institute for Evolutionary Anthropology Department of Genetics and Bioinformatics Group’s server . This set contains 3,865,304 SNPs identified from the sequences of 5008 chromatids. Based on recommendation from the authors [The package RAiSD version 2.9  was used authors  we omitthttps://github.com/ksiewert/BetaScan and installed on an in-house Linux computer cluster. The vcf files containing data from the 1000 Genomes project corresponding to human chromosome 19 was downloaded from NCBI (https://www.ncbi.nlm.nih.gov/genome/gdv/). This set contains 1,746,975 SNPs. Due to size-handling limitations in the vcf to BetaScan input file converter, glactools [The package BetaScan2  was usedlactools , we had S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableAfrican versus Non-African populations.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S11 Table(PDF)Click here for additional data file.S12 Table(PDF)Click here for additional data file.S13 Table(PDF)Click here for additional data file.S1 References(PDF)Click here for additional data file.


Drosophila melanogaster, comprising approximately one third of all SEZ neurons. We characterize the single-cell anatomy of these neurons and find that they cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. We find that the majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior.Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. Here, we generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult  Drosophila melanogaster is an excellent animal in which to investigate the basic principles of sensorimotor processing. Recent electron microscopy datasets provide unprecedented synaptic resolution of approximately 100,000 neurons that comprise the adult D. melanogaster brain  of the adult insect brain plays a critical role in many sensory-driven behaviors. Defined as the brain tissue below the esophageal foramen, it is situated in a central location between the motor circuits of the ventral nerve cord (VNC) and the higher order brain regions of the supraesophageal zone . In Droselection . It receelection . Two majelection . Recent election . DespiteDrosophila have provided genetic access to libraries of single neurons in other brain regions, including the mushroom body, central complex, and lateral horn  and stacked (inferior to superior) domains of organization in the SEZ. Furthermore, polarity analysis shows that many SEZ interneurons have inputs and outputs on the same processes, whereas SEZ projection neurons tend to be classically polarized, with inputs and outputs located in clearly distinct regions of the neuronal arbors. Taken together, the genetic reagents described here provide a valuable resource to investigate how diverse sensory inputs are processed by local SEZ circuitry to control specific behaviors.Here, we report the creation of 277 split-GAL4 lines that we collectively term the SEZ Split-GAL4 Collection. We estimate that this collection targets nearly one third of all neurons with cell bodies in the SEZ of the adult D. melanogaster brain , saddle (SAD), prow (PRW), and antennal mechanosensory and motor center AMMC; . These Ser brain  to deterer brain  in indivDeformed (Dfd) (mandibular and maxillary neuromeres), Sex combs reduced (Scr) , or labial (lab)   . Of the  and lab . TogetheTo translate the proportions derived from single-cell RNA-sequencing data into an estimate of SEZ cell number, we directly counted cell nuclei in SEZ neuromeres in individual brains. Two knock-in LexA lines, Dfd-LexA and Scr-LexA , were usDrosophila neurons to identify similar neurons across multiple samples. Cell types were defined as a pair or group of neurons with minimally variant morphology such that they were readily identifiable across multiple samples and driver lines by an expert. We used several strategies to identify novel SEZ cell types: (1) visual search through publicly available GAL4 collections  Lexlections ; and (4)lections . Ascendilections . Each nolections  to selecWe screened ~3400 split-GAL4 combinations using this strategy, which yielded 277 lines that provide precise access to single-cell types in the SEZ. The expression of each line is annotated to indicate the cell type that it was designed to target and the quality of the line . These shttps://splitgal4.janelia.org/.To evaluate the completeness of coverage achieved by the SEZ Split-GAL4 Collection, we compared the total number of neuronal cell bodies covered by the split-GAL4 lines with our SEZ neuron number estimates. SEZ cell types fall into either unique or population classifications, where unique neurons encompass a single pair of cell bodies while population neurons are small groups of cell bodies with nearly identical arbors . TherefoTo investigate SEZ organization at a cellular level, we used the NBLAST algorithm to perform automated clustering of SEZ cell types to define cell type supergroups . NBLAST The resulting supergroups share anatomical similarities and coordinates that reveal that the SEZ is organized into layered and stacked domains. Five of the six supergroups are layered from anterior to posterior: 1 and 2 most anterior, followed by 3, 5, and finally 6 most posterior. Groups 1 and 2 are in a similar anterior plane but group 1 is positioned superior to group 2. Group 4 sits below these domains, wrapping the inferior surface of the SEZ. A lateral view illustrates that group 5 appears to form a ‘roll’ shape and is surrounded by group 3 anterior, group 4 inferior, and group 6 posterior. For each group, we show the morphology of an individual, segmented neuron for each cell type —9 as welGroup 1 is composed of neurons that arborize in the prow and flange , the supGroup 2 contains only SEZ interneurons  plus oneGroup 3 contains 27 members and is composed of SEZ projection neurons and interneurons that overlap with the dendrites of these projection neurons . In the Group 4 contains interneurons that arborize in the inferior GNG and wrap the inferior surface of the GNG . Group 4Group 5 contains 27 interneurons, projection neurons, and DNs that arborize just posterior to the boundary between the anterior and posterior SEZ, flanked by groups 3, 4, and 6 on the anterior, inferior, and posterior sides, respectively . ProjectGroup 6 contains neurons in the posterior of the brain, spanning the GNG, inferior posterior slope, and superior posterior slope  and is tTo shed light on the structure of information flow both within the SEZ and out of the SEZ to the higher brain and VNC, we undertook polarity analysis of the 121 SEZ cell types that were segmented for NBLAST clustering analysis. These 121 cell types include 81 interneuron cell types, 26 projection neuron cell types, 13 DN cell types, and 1 sensory neuron cell type. We used both polarity staining with pre-synaptically localized HA-tagged Synaptotagmin and the smooth versus varicose appearance of neurites to score the presence of pre- and postsynaptic processes in each brain region in the central brain and VNC . Upon exAmong the SEZ interneuron cell types we analyzed, 39/81 (48%) have mixed polarity, 22/81 (27%) have biased polarity, and 20/81 (25%) are classically polarized. Interneuron types are distributed throughout the six cell type supergroups with most groups containing interneurons of all polarity classes. However, all group 2 interneurons (making up 17/18 cell types in group 2) have either biased or mixed polarity. This suggests that the interneurons of group 2 may participate in reciprocally connected circuits. Among interneuron cell types that are clearly polarized, there were some cases in which no axon was evident in the brain . In these cases, the presence of severed processes suggests that these cell types may not be interneurons and may instead send projections out of the central nervous system.Most SEZ projection neurons analyzed in this study are clearly polarized . However, a few have biased  or mixed polarity . Polarized cell types belong to several cell type groups  and project to numerous brain regions including the lobula and the superior, inferior, and ventrolateral neuropils. In contrast, all projection neuron cell types with biased polarity have axons in the SMP and belong to group 1. The high proportion of clearly polarized SEZ projection neurons suggests that they commonly carry information unidirectionally from the SEZ to other brain regions. We did not identify any projection neurons that link the SEZ directly to the central complex or the mushroom body. In addition to projection neurons, the SEZ Split-GAL4 Collection includes one novel sensory neuron class (diatom) that is polarized, with dendrites in the proboscis labellum and SEZ axonal projections .Analyzing the polarity of the novel SEZ DNs described here, we find that 5/13 (39%) have biased polarity, 2/13 (15%) have mixed polarity, and 6/13 (46%) are clearly polarized. These DNs have outputs in the neck, haltere, and leg neuropils , and in the abdominal ganglion, consistent with the observation that GNG DNs are frequently connected with the leg neuropils .Thus, polarity analysis reveals that most types of SEZ interneurons have either mixed or biased polarity. In contrast, SEZ projection neurons are frequently clearly polarized while the SEZ DNs reported here are mostly mixed or polarized. These distinct polarization strategies may reflect the functional roles that interneurons, projection neurons, and DNs play in SEZ circuits. Interneurons in the SEZ may participate in reciprocally connected local networks while projection neurons and DNs may primarily relay information from the SEZ to other brain regions.Here, we describe the SEZ Split-GAL4 Collection, a library of 277 split-GAL4 lines covering 138 SEZ cell types, which affords unprecedented genetic access to SEZ neurons for behavioral and functional study. Our studies provide insight into the diversity of SEZ cell types and their organization into discrete anatomical domains. The SEZ Split-GAL4 Collection will enable further investigation of how local SEZ circuitry and ascending SEZ paths process sensory inputs and control specific behaviors.Most of the SEZ Split-GAL4 lines are specific, with 149/277 lines classified as ideal or excellent. These lines will be useful to manipulate individual SEZ cell types for behavioral, functional, and imaging experiments. The remaining, less specific, lines (those belonging to the good or poor categories) will still be useful for imaging and as starting points for creating more specific reagents. Good and poor lines may be used to generate CDM masks to search for new hemidrivers to make further split-GAL4 lines. Alternatively, their expression patterns may be refined using Killer Zipper or three-way intersections with LexA or QF lines . All linD. melanogaster, it is not possible to assign cell bodies to defined neuropil regions without a genetic marker. The advantage of our method of estimating SEZ neuron number is that it is based on analysis of the four genetically defined SEZ neuromeres, the tritocerebral, the mandibular, the maxillary, and the labial neuromeres. However, previous reports demonstrate that some deutocerebral commissures cross below the esophageal foramen, and therefore an unknown number of deutocerebral cell bodies may be part of the SEZ .Either Dfd-LexA or Scr-LexA was crossed to a reporter line with LexAop-nls-GCaMP6s (this work) and His2Av-mRFP . Labial-The following primary antibodies were used:1:40 mouse α-Brp (nc82) 1:1000 chicken α-GFP (Invitrogen A10262)1:1000 rabbit α-dsRed The following secondary antibodies were used:1:500 α-mouse AF647 1:1000 α-chicken AF488 1:1000 α-rabbit AF568 https://www.janelia.org/project-team/flylight/protocols, ‘IHC-Anti-GFP’) substituting the above antibodies and eschewing the pre-embedding fixation steps. Ethanol dehydration and DPX mounting was carried out as described . Images were acquired with a Zeiss LSM 880 NLO AxioExaminer at the Berkeley Molecular Imaging Center. A Plan-Apochromat 63×/1.4 Oil DIC M27 objective was used at zoom 1.0. Acquired images had a voxel size of 0.132 μm × 0.132 μm × 0.500 μm. Expression in the SEZ was imaged in a tiled fashion and then stitched in Fiji using the ‘Grid/Collection stitching’ plugin with ‘Unknown Positions’ and ‘Linear Blending’ . Following dissection, staining, and mounting, split-GAL4 combinations were screened by eye using epifluorescence on an a LSM710 confocal microscope (Zeiss) with a Plan-Apochromat 20×/0.8 M27 objective. We assessed the specificity of each line in the central nervous system only, not in peripheral tissues. Imagery was viewed and organized using the Janelia Workstation A text description of any off-target expressionTypes of imagery collected, including polarity and MCFO datahttps://www.janelia.org/project-team/flylight/protocols). When crossed to 20XUAS-CsChrimson-mVenus (attP18);; dissection and staining were carried out by FlyLight according to the FlyLight ‘IHC-Polarity Sequential Case 5’ protocol (https://www.janelia.org/project-team/flylight/protocols). MCFO characterization of stable split-GAL4 lines was accomplished by crossing stable lines to MCFO-2 or MCFO-3 (see Key resources table for full genotypes). If crossed to MCFO-2, adult flies were heat shocked at 37° C for either 30 or 60 min 1 day after eclosion. Dissection and staining of MCFO samples were carried out by FlyLight according to the FlyLight MCFO staining protocol . Samples stained for polarity and MCFO analysis were first imaged on an a LSM710 confocal microscope (Zeiss) with a Plan-Apochromat 20×/0.8 M27 objective. Then, sample images were viewed using the Janelia Workstation . A single example of each cell type targeted by the collection was selected for segmentation in VVDviewer  created as described above were compared against established neuropil regions  in VVDvi public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Acceptance summary:Drosophila community. Specifically, we appreciate the careful anatomical characterisation of the SEZ area, one of the less well characterised brain regions in the central brain of insects. We anticipate that your work will be of great interest and help to researchers interested in understanding the representation of taste as well as the control of movement.We believe that your work represents an important resource of information and tools to the Decision letter after peer review:Drosophila brain" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Ronald Calabrese as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Kristen Lee (Reviewer #2).Thank you for submitting your article "Classification and genetic targeting of cell types in the primary taste and premotor center of the The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential Revisions (for the authors):1) Please address the recommendations of the reviewers to improve figure clarity and presentation.2) The reviewers suggest additional citations and additional discussion points. Please address these by adding appropriate citations and paragraphs in the text.3) You will see that some additional experiments were suggested. Please consider adding additional experiments, if possible.Reviewer #1 (Recommendations for the authors):– Line 78: delete "of"– Line 92-98: I appreciate the authors being candid about the limitations here.– Line 111: why were cell types from AMMC not included? Perhaps can be explicit here.– Line 152: maybe I missed this, but I was wondering what the source of the SEZ neuron number estimates is.– Lines 276-279: I think this paragraph can be omitted in the Results section, since the same points are made in the Discussion and can be somewhat repetitive.– Line 295: to me it seems more logical to have the quotation marks around "classically" rather than "polarized" to follow along with "mixed" and "biased" polarity. Later the authors use "clearly" polarized, so maybe say clearly polarized already in this line for consistency.– Lines 336-339: This sentence can be revised for additional clarity.– Line 564 show -> "shown".– Figure 1: I think changing one set of color schemes will be useful, instead of using the same set of colors to label regions and quality of the gal4 line.– Figure 2: is the vertical axis and tick marks meaningful in A? Also, perhaps enlarging the group #'s can help with visibility.– Figure 6 supplement 2 is misplaced.– Figure 4 supp 2: Please include the split line in the figure.Reviewer #2 (Recommendations for the authors):Result comments:Ito, 2014 refers to all structures below the level of the esophagus as SEZ. They also annotate the cantle (CAN) and flange (FLA) as a periesophageal neuropil. Why are they not listed an anatomical regions of the SEZ on line 75? Especially since the flange is mentioned on line 207.The Results section would greatly improve by mentioning in some more detail how the different cell types  were determined, since they are a main focus of the figures, but rarely mentioned in the text. I found this discrepancy to be a little confusing/distracting.Although the polarity data is interesting, I am curious as to whether this is a surprising finding or not? I am not acutely familiar with that field, and think some more speculation on this in the discussion or results would enhance reader understanding. What are the impacts of this result?Figure comments:Figure 1D, E: the labeling is confusing. I think the figure would benefit from describing what each color in the confocal image represents. There also seems to be a lot of noise from connective tissue/the neck connective which is a little distracting. If irrelevant, could this be stitched out?Figure 1 – Supplement 1: Could the SEZ or esophagus be marked with a dash line? This will better orient the reader with the anatomical regions. Could this figure also be flipped to match the orientation of Figure 1 A, D, E?Figure 1G feels out of place in Figure 1, and may fit better in Figure 2.Figure 9 could benefit from a supplemental figure showing examples of each polarity type.Reviewer #3 (Recommendations for the authors):A) Suggestions for improved or additional experiments:In analysis like this, there´s always the issue of where one should draw the line in terms of providing some/any kind of physiological/functional data, whether these are stainings with functionally relevant markers , behavioral studies, functional imaging, etc. In this case, there appears to be some instances where it´s screaming out: e.g., the IN1 or FDG. Both of these neurons have been published with extensive functional data, and it would be great to see if these are the same neurons indeed and to confirm it with some functional analysis.B) Recommendation for improving the writing and presentation (in no particular order):1) The authors mention different degree of line cleanness, and say rightly that some are more appropriate for behavioral analysis, others sufficient for imaging. However, these are based on stainings from CNS. Is is known how many of these "CNS clean" lines presented here label peripheral tissues, whether neuronal or non-neuronal tissues?2) Nomenclature: two types of names are given here, one is the JRC number and the other is a "private" name. How would the nomenclature proceed in subsequent analysis, when for example, a second neuron which looks very similar to one presented here is identified? Would this be considered as belonging to the same class . There is one named FDG-medial, invoking FDG; on what basis is this given this, rather than a completely separate name). This is not a criticism of the current naming at all, just wondering how it will play out as the SEZ coverage increases in the future, and more closely related/looking neurons become identified.3) A major section is given to analysis of neuronal polarity based on bioinformatic analysis. Some selected illustrative examples would help , and show in real anatomy what terms like mixed, biased and polarized actually look like.4) This paper is focused on adult SEZ, and appropriately refers to earlier works in adults; but in some instances, it refers to analysis from embryo/larva ; subdivisions of the SEZ based on synaptic connectivity have been described in larva  but not mentioned here. Based on this, perhaps the authors can comment on what parts of the larval data are or are not relevant for their own work, or clearly state the works of adult vs larva or change the wording a bit . Reviewer #1 (Recommendations for the authors):– Line 78: delete "of"done– Line 92-98: I appreciate the authors being candid about the limitations here.Thank you– Line 111: why were cell types from AMMC not included? Perhaps can be explicit here.The AMMC is an anatomically segregated region. Other researchers are generating split-Gal4s for this region. We noted and justified not including the AMMC on lines 150-152.– Line 152: maybe I missed this, but I was wondering what the source of the SEZ neuron number estimates is.These estimates come from the first section of the results.– Lines 276-279: I think this paragraph can be omitted in the Results section, since the same points are made in the Discussion and can be somewhat repetitive.We deleted this paragraph.– Line 295: to me it seems more logical to have the quotation marks around "classically" rather than "polarized" to follow along with "mixed" and "biased" polarity. Later the authors use "clearly" polarized, so maybe say clearly polarized already in this line for consistency.We agree that the quotation marks are inconsistent. We removed the quotation marks for increased clarity.– Lines 336-339: This sentence can be revised for additional clarity.We edited this sentence.– Line 564 show -> "shown".corrected– Figure 1: I think changing one set of color schemes will be useful, instead of using the same set of colors to label regions and quality of the gal4 line.Based on all reviewers’ comments, we removed 1G from Figure 1 and included a new Figure 2 to describe the quality of the split-GAL4 lines.– Figure 2: is the vertical axis and tick marks meaningful in A? Also, perhaps enlarging the group #'s can help with visibility.The vertical axis represents the distance or dissimilarity between the clusters, noted in the figure legend. We have enlarged the group numbers to make them more visible.– Figure 6 supplement 2 is misplaced.corrected– Figure 4 supp 2: Please include the split line in the figure.Figure 5 —figure supplement 2 now includes the expression pattern of the split-GAL4 line.Reviewer #2 (Recommendations for the authors):Result comments:Ito, 2014 refers to all structures below the level of the esophagus as SEZ. They also annotate the cantle (CAN) and flange (FLA) as a periesophageal neuropil. Why are they not listed an anatomical regions of the SEZ on line 75? Especially since the flange is mentioned on line 207.Drosophila, the saddle, AMMC, and prow lie below the esophagus and can therefore be considered as parts of the SEZ (but not of the GNG).”The CAN and FLA are not considered to be part of the SEZ since they are lateral to and not inferior to the esophageal foramen, as stated in pp. 51 of the Supplemental Information of Ito, 2014: “In certain insect species, some of the neuropils in the PENP lie below the esophagus and have therefore been regarded as parts of the SEZ . In The Results section would greatly improve by mentioning in some more detail how the different cell types  were determined, since they are a main focus of the figures, but rarely mentioned in the text. I found this discrepancy to be a little confusing/distracting.We have included a description of how cell types were determined in the Results section (lines 138-142 and lines 158-163)Although the polarity data is interesting, I am curious as to whether this is a surprising finding or not? I am not acutely familiar with that field, and think some more speculation on this in the discussion or results would enhance reader understanding. What are the impacts of this result?We have added the following discussion on polarity (lines 446-458): “Our studies also shed light on information flow both within the SEZ and out of the SEZ to the higher brain. We identified 82 local interneurons, 26 projection neurons, 12 descending neurons, and 1 sensory neuron. Polarity analysis of the 121 SEZ cell types revealed that SEZ interneurons tend to have mixed or biased polarity while SEZ projection neurons tend to be classically polarized. Polarity analyses of the lateral horn, mushroom body, descending neurons, and protocerebral bridge identified few neurons with mixed polarity (refs). Unlike these brain regions, the SEZ contains a large number of local interneurons. The mixed polarity of the SEZ interneurons argues for local and reciprocal connectivity between neurons, with information flowing in networks rather than unidirectional streams. Projection neurons, in contrast, may serve chiefly to pass information from highly interconnected SEZ circuits to other brain regions in a unidirectional manner.”Figure comments:Figure 1D, E: the labeling is confusing. I think the figure would benefit from describing what each color in the confocal image represents. There also seems to be a lot of noise from connective tissue/the neck connective which is a little distracting. If irrelevant, could this be stitched out?We replaced the stitched Dfd and Scr example images with lower resolution example overview images of the samples used to count cell number . We describe the colors in the legend but due to the reuse of colors in the figure we think that adding a color legend in the figure itself may be confusing.Figure 1 – Supplement 1: Could the SEZ or esophagus be marked with a dash line? This will better orient the reader with the anatomical regions. Could this figure also be flipped to match the orientation of Figure 1 A, D, E?We now include asterisks to mark the location of the esophageal foramen in Figure 1D and E as well as in Figure 1 —figure supplement 1. Furthermore, we have rotated the image in Figure 1 —figure supplement 1 to match the orientation of Figure 1 D and E.Figure 1G feels out of place in Figure 1, and may fit better in Figure 2.To address this, we moved Figure 1G to a new Figure 2 that is dedicated to describing the quality of the lines in the collection.Figure 9 could benefit from a supplemental figure showing examples of each polarity type.Thank you for your suggestion. We have included a new figure, Figure 10 —figure supplement 1 showing two examples of cell types belonging to each polarity class.Reviewer #3 (Recommendations for the authors):A) Suggestions for improved or additional experiments:In analysis like this, there´s always the issue of where one should draw the line in terms of providing some/any kind of physiological/functional data, whether these are stainings with functionally relevant markers , behavioral studies, functional imaging, etc. In this case, there appears to be some instances where it´s screaming out: e.g., the IN1 or FDG. Both of these neurons have been published with extensive functional data, and it would be great to see if these are the same neurons indeed and to confirm it with some functional analysis.We appreciate this suggestion and include data showing that three of the new Fdg split-GAL4 lines overlap with Fdg in the original lines by co-labeling studies . Although we confirmed the identity of Fdg, functional studies are beyond the scope of the study. We did not make a split-GAL4 for IN1.B) Recommendation for improving the writing and presentation (in no particular order):1) The authors mention different degree of line cleanness, and say rightly that some are more appropriate for behavioral analysis, others sufficient for imaging. However, these are based on stainings from CNS. Is is known how many of these "CNS clean" lines presented here label peripheral tissues, whether neuronal or non-neuronal tissues?We did not assess the expression of the lines outside of the central nervous system with the exception a line for the novel cell type diatom (SS40945), for which we looked at expression in the proboscis. We have clarified this in the methods (lines 593-594).2) Nomenclature: two types of names are given here, one is the JRC number and the other is a "private" name. How would the nomenclature proceed in subsequent analysis, when for example, a second neuron which looks very similar to one presented here is identified? Would this be considered as belonging to the same class . There is one named FDG-medial, invoking FDG; on what basis is this given this, rather than a completely separate name). This is not a criticism of the current naming at all, just wondering how it will play out as the SEZ coverage increases in the future, and more closely related/looking neurons become identified.We envision that NBLAST searches and co-labeling studies will be essential to determine if neurons are the same or different in different lines. The question of whether neurons belong to the same class is interesting but will not be resolved by anatomical studies alone. As the tools available for EM cell type analysis progress, it is possible that our naming scheme will be supplanted by standardized cell type names based on comprehensive reconstruction of all cells in the SEZ. Regarding the similarity of cell type names to previously reported neurons, we agree with the reviewer that the names FDG-medial and IN2 are not appropriate and have renamed them to neutral names  that do not suggest similarities to previously studied neurons.3) A major section is given to analysis of neuronal polarity based on bioinformatic analysis. Some selected illustrative examples would help , and show in real anatomy what terms like mixed, biased and polarized actually look like.We have now included a new figure  showing examples of different neural polarities, including Fdg, G2N-1, TH-VUM, and diatom. We also show examples of cell types in the mixed, biased, and polarized classes. Our apologies again for the confusion between IN1 and IN2. We did not make any split-GAL4 lines for IN1 but named IN2 based on its similar location in the brain. We have renamed IN2 “aster” to avoid this confusion for future readers.4) This paper is focused on adult SEZ, and appropriately refers to earlier works in adults; but in some instances, it refers to analysis from embryo/larva ; subdivisions of the SEZ based on synaptic connectivity have been described in larva  but not mentioned here. Based on this, perhaps the authors can comment on what parts of the larval data are or are not relevant for their own work, or clearly state the works of adult vs larva or change the wording a bit .We appreciate this suggestion, added the omitted reference, and altered the wording (lines 49-50).


Stroke is a multiphasic process, and the initial ischemic phase of neuronal damage is followed by secondary innate and adaptive responses that unfold over days after stroke, offer a longer time frame of intervention, and represent a novel therapeutic target. Therefore, revealing the distinct functions of immune cells in both brain and periphery is important for identification of immunotherapeutic targets for stroke to extend the treatment time window. In this paper an examination of the cellular dynamics of the immune response in the central nervous system (CNS) and periphery provoked by cerebral ischemia is provided. New data is presented for the number of immune cells in brain and spleen of mice during the 7 days following middle cerebral artery occlusion (MCAO). A novel analysis of the correlation among various cell types in the brain and spleen following stroke is presented. It is found that the infiltrated macrophages in the ischemic hemisphere positively correlate with neutrophils which implies their synergic effect in migrating into the brain after stroke onset. It is noted that during infiltration of adaptive immune cells, the number of neutrophils correlate positively with T cells, which suggests neutrophils contribute to T cell infiltration in the stroked brain. Furthermore, the correlation among neurological deficit and various immune cells suggests that microglia and splenic adaptive immune cells (T and B cells) are protective while infiltrating peripheral myeloid cells (macrophage and neutrophils) worsen stroke outcome. Comprehension of such immune responses post cerebral ischemia is crucial for differentiating the drivers of outcomes and also predicting the stroke outcome. The time window for vascular intervention after stroke, using either thrombolytic therapy or endovascular surgery, is limited and neuroprotective approaches have failed in clinical trials. On the other hand, stroke is a multiphasic process with the progression of ischemic brain injury associated with intense and long-lasting innate and adaptive immune responses , 2 that via the data and analysis.From a translational perspective, several metrics have been used to quantify the harmful effects of cerebral ischemia. The degree of ensuing weight loss has been viewed as a useful measure, as have the infarct volume and neurobehavioral outcomes including the loss of motor function. In this work the neurological deficit score (NDS)  is measuThis study was conducted in accordance with the National Institutes of Health (NIH) guidelines for the use of experimental animals. Animal protocols were approved by the Institutional Animal Care and Use Committee (IACUC). Wild-type C57BL/6 male mice were purchased from Jackson Laboratories . All mice were housed in an environment controlled for lighting (12-hour light/dark cycle), temperature, and humidity, with food and water available ad libitum.Mice of 8-12 weeks were randomized and subjected to sham surgery or 45 minutes of occlusion of middle cerebral artery (MCA) followed by reperfusion, with survival out to 7 days maximum. Transient middle cerebral artery occlusion-reperfusion (tMCAo-RP) experiments were performed as described previously . In brievia centrifuge as supernatant. Splenocytes were re-suspended in 200 µl 0.01MPBS for flow cytometry. Brain hemispheres were homogenized in 10ml 1x Hank’s balanced salt solution , and filtered through a 70µm cell strainer. After wash with 0.01M PBS, myelin digestion was then carried out with collagenase IV  incubation for 30 min at 37°C and shaking at 200 rpm. Myelin was separated by density gradient centrifugation (1500xg for 7 min) in 30% Percoll . Cells were resuspended in 200ul PBS for flow cytometry. Dead cells were identified using LIVE/DEAD® Fixable Dead Cell Stain Kit  according to the manufacturer’s protocol. Cells were fixed with 1.6% paraformaldehyde (PFA in 1xPBS) for 10min at room temperature and stored at -80°C for flow cytometry.Mice were anesthetized with isoflurane and then transcardially perfused with 0.9% saline (wt/vol), followed by collection of brain and spleen samples. Spleen samples were homogenized in 10ml 0.01 M PBS and filtered through a 70 μm cell strainer to isolate single cells. These cells were then concentrated by centrifugation as pellets followed by incubation in 3ml RBC lysis buffer  for 5 min on ice to lysis the red blood cells. The lysed red blood cells were then eliminated 6 cells were suspended in 200 μl HBSS buffer and incubated with anti-mouse CD16/32  for 10 min at 4°C to block Fc-receptor binding. Dead cells were discriminated using Aqua Amine according to the manufacturer’s protocol . The following antibodies were used for surface receptor detection to identify neutrophil, macrophage, microglia, T cells and B cells: CD45 , CD11b , Ly6G , CD3  and CD19 . Stained Cells were then washed with HBSS buffer, resuspended in 200 μl of HBSS and analyzed with a LSRII cytometer  and FlowJo software . In particular, neutrophil is identified in the hiCD11b+Ly6g+, macrophage as CD45hiCD11b+Ly6g-, microglia as CD45medCD11b+, T cells as CD45+CD11b-CD3+CD19- and B cells as CD45+CD11b-CD3-CD19+.All samples were acquired as described above. ~10in situ apoptosis and proliferation have not been considered. The variables that shall be used throughout this work to refer to various processes are listed in We consider three successive temporal intervals as being reflective of the cellular phenomena that follow the insult and discuss quantifiable parameters for each stage. Due to the lack of astrocyte and neuron data, we restrict attention to macrophages, microglia, neutrophils, dendritic cells, and lymphocytes. A description of the three stages is provided as we consider the temporal progression of the immune response following MCAO in mice. A primary aim of the analysis is to elucidate the dynamics of immune cells that travel to the brain and spleen following the insult. As with virtually any biological modeling effort, the presented analysis will be a simplification and not a comprehensive account of the underlying reality. For instance, cell count changes due to via the blood stream. Neutrophils are believed to arrive later than the macrophages, approximately 24 hours after the insult, and contribute to injury  are present in the peripheral blood post-MCAO; whereas the immune cells on the left-hand-side are in the brain post-MCAO. With the suggested relations in (1) and prospective data, one could calculate estimates for the retention probabilities. Ultimately, having reliable estimates of the retention probabilities, and a measurement of the number of immune cells in the peripheral blood would allow one to predict the number of immune cells that adhere to the area of infarct, predict the infarct volume, and potentially infer the stroke outcome.The Pearson correlation (via corrcoef in MATLAB R2020a) was used to assess the prospective statistical dependence among the dynamics of the various cell types in the brain and spleen as well as the statistical dependence among the number of cells and the NDS. The number of animals used in computing the mean of a cell type for a particular day ranges from 5 to 14  of 30, 45, and 60 minutes . FactorsWe consider the interval 0 < t ≤ 72 hours as the acute stage immediately following reperfusion. The earliest part of this stage contains the juncture 0 < t < 12 hours when the infiltration of myeloid cells start, microglia become activated, and cytokines are released from the brain . Commencin situ whereas the macrophages require time to arrive from different locations. Macrophages arrive at the area of infarction within the first two days after the activation of the microglia  or an anti-inflammatory (M2) phenotype while recognizing that each contains multiple sub-phenotypes. Importantly, macrophages of either M1 or M2 phenotype can perform phagocytosis, and there are two general functions that are not exclusive to the phenotype. The first function is deemed beneficial and consists of clearing debris and dead tissue as well as releasing trophic factors that help neurons survive during the ischemic response. The second function is deemed deleterious and consists of release of toxic cytokines and free radicals.Over the initial 96 hours post-stroke, immune cells in the spleen undergo cell death as well as cell division and migration out of the spleen. The spleen shrinks during this stage with higher levels of shrinkage typically corresponding to a larger infarct volume. The infiltration of the macrophages, neutrophils, and dendritic cells into the ischemic brain has been shown to precede that of the lymphocytes –22. The The number of myeloid cells in the spleen, initially decline over the first 2-4 days after the ischemic event, begin to recover after 96 hours and normalize by day 7 post stroke  as shownThe dynamics of the number of microglia in the cortex and striatum appears similar for the M1 and M2 phenotypes . A findivia high expression levels of CD45, and the amount of T-cell activation was identified via expression of the CD25 and CD69 markers. For the data presented in Gelderblom et al. , the autThe data from  indicateWhile no contralateral neutrophil data was provided in , FigureThe spleen has been implicated as a primary source of peripheral immune cells infiltrating into the ischemic brain. The change in spleen size after ischemic injury and its negative association with infarct volume are likely linked , 24 as svia other means such as lymphatic vessels; thus focusing on blood may not reflect the immune cells migration from the periphery to the CNS. Furthermore, cell counts can be attained from the spleen but not from blood since it is not feasible to measure the entire population of immune cells from blood. This is because it is not possible to attain the blood volume from each animal since the blood level is reduced following stroke with individual variability. For each time point after stroke, the mean of every measured cell type was computed across the mice in order to attain an aggregate account across the population. Subsequently, Pearson correlation coefficients were computed over different intervals of time post infarct – this included day 0-7, and its constituent intervals of day 0-3 and day 4-7. This information is contained in the heatmaps in Having knowledge of the extent and nature of the correlation among variables in a study is crucial from predictive and causative perspectives. Discovering the correlation among immune cells at various stages of stroke is particularly valuable since it might not be possible to measure the dynamics of all cell types during an experiment. We consider the spleen instead of blood as the peripheral source of leukocytes because the spleen is a primary source of infiltrated immune cells to the brain after stroke. Although immune cells may traffic into the brain through blood circulation, they may also migrate ρ| > 0.8). Also, there is a 212.5% increase (8 to 25) in comparing the number of relations for 4-7 days to those for the 7-day period (both |ρ| > 0.9). For the 0-3 day post-infarct interval The above analysis was repeated with the Pearson correlation coefficients computed separately for data spanning the two temporally-distinct intervals of 0-3 days and 4-7 days post-infarct. We note from via cell tracking experiments, it does provide correlational evidence. For instance, The relationship among the cell types during the 4-7 day post-insult interval is illustrated in The relationship between the NDS and the time after MCAO is shown in A dearth of computation currently exists to characterize the inflammatory processes associated with cerebral ischemia. This work presents analysis to quantify the accumulation of immune cells following MCAO-induced cerebral ischemia in mice. By developing a model to quantify the biological occurrences, we hope to have motivated a means to study the cellular dynamics associated with cerebral ischemia. For instance, while there has been no concrete conclusion on the function of peripheral myeloid cells and microglia post stroke, the presented correlational analysis suggests that microglia may have a protective effect while infiltrating peripheral myeloid cells likely exert detrimental effects at an early stage post stroke , LTc(t), and Z(t). The temperature during an experiment is frequently not constant across different studies. Increased temperature is detrimental to the survival of neurons and may cause damage to all of the mentioned cell types. In fact, higher than normal temperatures contribute to a larger infarct volume while hypothermia has been shown to reduce the pathology associated with stroke , Y(t), D(t), Lh stroke –40. It svia a combination of induced MCAO, flow cytometry, neurological deficit scoring, and statistical analysis. Several advancements are necessary for the presented work to be part of a large effort to quantify and predict cellular dynamics associated with cerebral ischemia and outcome. An intriguing study would entail correlating the infarct volume of a subject with the rates of cell accumulation in the ischemic brain. As additional data becomes available it will be possible to incorporate additional variables. For instance, the location of the infarct in the brain is a crucial determinant for the loss of motor function. A relatively small infarct volume centered in the motor cortex would be expected to cause a devastating loss of motor function. This is in contrast to white matter/lacunar strokes that can show faster recovery time in comparison to a gray matter large vessel stroke (The results communicated in l stroke . Anotherl stroke , 43. via quantitative analysis. We have found that the infiltrated macrophages in the ischemic hemisphere positively correlate with neutrophils. This implies their synergic effect in migrating into the brain after stroke. It was also observed that during infiltration of adaptive immune cells, the number of neutrophils correlate positively with T cells suggesting that neutrophils contribute to T cell infiltration following stroke. This was also supported by observation in the ipsilateral brain as large changes in the number of neutrophils occurred earlier than that in T cells. The correlational analysis supports the concept that microglia have a protective effect while infiltrating peripheral myeloid cells exert detrimental effects in stroke outcome. Although the conclusions are based on correlational rather than casual evidence, they constitute a quantitative basis for understanding the brain and spleen immune cell dynamics following cerebral ischemia.In summary, the quantitative analysis of cell dynamics and the correlational study presented serve to further elucidate the interactions among various cell types following stroke. While spleen weights and morphometrics following stroke has been reported in prior works such as , splenicThe original contributions presented in the study are included in the article/The animal study was reviewed and approved by Stanford Institutional Care and Use Committees.SS analyzed the data and wrote the manuscript. QL designed the experiments, interpreted the data, and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.