Inbred lines are important germplasm in cauliflower breeding programs. To understand the genetic diversity and relationships of cauliflower inbred lines, the use of simple sequence repeat (SSR) markers will be of great value for parental line selection and breeding strategy design. In this study, the genetic diversity and relationships of 165 cauliflower inbred lines primarily derived from southeast China were assessed using SSR markers. Forty-three SSR markers were polymorphic across these inbred lines and generated a total of 111 alleles. The mean values of the number of alleles (Na), effective number of alleles (Ne), Shannon’s Information index (I), and polymorphism information content (PIC) per locus were 2.581, 1.599, 0.517 and 0.316, respectively. Genetic distance values among all pairs of the inbred lines varied from 0 to 0.67 with an average of 0.30. On the basis of genetic distance data estimated with the SSR markers, the 165 cauliflower inbred lines were classified into four main clusters (from group Ⅰ to group Ⅳ) by cluster analysis and four subpopulations (from POP 1 to POP 4) by structure analysis. The classification patterns of most cauliflower inbred lines were not consistent with their curd maturity, curd solidity or geographic origins. These results based on estimates by the SSR markers, suggested the genetic diversity of the 165 cauliflower inbred lines was relatively narrow. Therefore, pyramiding the valuable genes among different types of the cauliflower inbred lines is important to increase the genetic diversity to obtain desirable hybridization combinations. The information generated in this report will be useful for assessing germplasm and breeding in cauliflower. Brassica oleracea var. botrytis) is one of the important horticultural crops with an annual global production of over 25 million tons [Cauliflower  using a limited number of SSR markers and found that cauliflower cultivars had low genetic diversity among the three botanical varieties of B. oleracea [Traditionally, the genetic diversity and relationships of germplasm have usually been assessed based on morphological data, pedigree data, or geographic information on the origin of the materials –6. Curreoleracea –3. AdditAlthough some genetic diversity and relationship studies were conducted with SSR markers in cauliflower, few previous studies involved cauliflower inbred lines from China. We obtained many inbred lines by self-selecting during cauliflower breeding programs since 1997. However, these inbred lines are rarely effectively utilized in our breeding programs. To increase utilization of the cauliflower inbred lines, we assessed phenotypic variation and diversity of 165 inbred lines and found that these inbred lines exhibited a wide degree of variability for most phenotypic traits . In thisOne hundred and sixty-five (165) cauliflower inbred lines were used for genetic diversity and relationship determination, which were bred by artificial self-selecting from different cauliflower cultivars collected from different markets . These cauliflower cultivars were primarily derived from southeast China and other regions: Fujian (66), Zhejiang (44), Taiwan (25), Shanghai (5), Hong Kong (4), Jiangxi and Japan (3 each), Henan and Hunan (2 each), Chongqing, Nederland and Italia (1 each), unknown region (8). The cauliflower inbred lines are stored in the Wenzhou Academy of Agricultural Sciences, Wenzhou, Zhejiang Province, China. Detailed information on the original accessions and the inbred lines is listed in 2, and then added to a 1.5 ml centrifuge tube. Extraction buffer, 600 μl , was then added, and the tube was vortexed and incubated for 30 min in a 65°C water bath. The tube was cooled to room temperature, 600μl of chloroform-isoamyl alcohol (24:1) was added, and then tube was mixed gently for 30 min and centrifuged at 8,000 rpm for 15 min. The supernatant was transferred to another 1.5 ml tube, 700 μl of precold alcohol (-20°C) was added and the tube was mixed gently to precipitate the DNA. The mixture was centrifuged at 12,000 rpm for 6 min, and the supernatant was removed carefully. The DNA pellet was washed with 400μl of 70% (v/v) alcohol five times and then air-dried and resuspended in 200μl of ddH2O. The DNA concentration was adjusted to 50 ng/μl based on spectrophotometer readings  Instrument Co., LTD., Shanghai, China), and the DNA was preserved at -20°C.Total genomic DNA was extracted from young leaves of 3-week-old seedlings of each inbred line using the protocol of Shi and Hong with modification . A leaf http://www.brassica.info/resource/markers.php. A total of 509 SSR primer pairs were prescreened for polymorphism with ten distinct commercial varieties of cauliflower . Primers were excluded from the study when they did not produce different or unambiguous band sizes. A final set of 43 SSR primers were selected for further analysis  as follows: an initial denaturation step for 5 min at 94°C; 40s at 94°C, 40s at annealing temperature relying on the primers, and 1 min at 72°C for 35 cycles, followed by an extension step of 10 min at 72°C. Then, the reaction was stopped at 6°C.3 staining solution for 12 min. Then, the stained gel was wash twice with ddH2O and placed in 1.5% NaOH containing 1% formaldehyde solution for approximately 10 min to obtain the visible fragments.Ten microliters of the final PCR amplification product was mixed with 1μl of loading buffer . Two microliters of the sample was loaded onto a 6% acrylamide-bisacrylamide gel (19:1) in 1×TBE, and electrophoresed at 150 volts for 100–150 min. The fragments were detected using a silver staining procedure as follows. The peeled gel was fixed with 10% ethanol containing 0.5% acetic acid solution for 12 min. The fixed gel was placed in 0.1% AgNOpi)2, where pi is the frequency of ith allele in a population [The DNA fragment size amplified by SSR primers was determined based on DNA molecular marker size (range 100bp to 2000bp). The number of alleles (Na), effective number of alleles (Ne), and Shannon’s Information index (I) per locus were estimated using POPGEN version 1.32 software . The polpulation  was usedpulation . Princippulation . Populatpulation . Ten indAll 43 prescreened SSR primers were used to detect the molecular variance among the 165 cauliflower inbred lines . The parNei’s (1972) genetic distance matrix data were calculated for all inbred lines on the basis of the SSR markers. To estimate the genetic diversity and relationships among these inbred lines, cluster analysis was performed based on the genetic distance matrix data estimated using SSR markers. The dendrogram constructed by neighbor-joining (NJ) cluster analysis showed that the clustering pattern was complex . All 165Principle coordinate analysis (PCA) was chosen to complement the cluster analysis information and genetic relationships . AssociaStructure analysis using the 43 SSR markers revealed that the LnP(D) (log-likelihood) increased with the model parameter K value . The staThe genetic diversity of the four groups  evaluated by the SSR markers is shown in The genetic diversity of different maturity populations assessed by the SSR markers is presented in The genetic diversity of different curd solidity populations assessed by the SSR markers is shown in Fujian, Zhejiang and Taiwan are the areas in which early cauliflower breeding programs were conducted in China. To clearly understand the genetic variation in cauliflower inbred lines derived from these areas, the genetic diversity was also estimated by the SSR data . The inbBrassica oleracea genotypes (including 51 cauliflower varieties) estimated by Zhao et al. [Molecular marker types and population size are the factors that influence the evaluation of genetic diversity and relationships. SSR markers play an important role in establishment of genetic diversity and relationships because they are reproducible, polymorphic, codominant and abundant in plant genomes –36. FortBrassica napus L [Genetic distance estimates can be used as the basis for effective utilization of the inbred lines with diverse genetic backgrounds for hybridization in breeding programs . The gen napus L . Most of napus L .Cluster analysis and principle coordinate analysis are helpful for breeders to understand the genetic diversity and relationships of parents to obtain desirable hybridization combinations , 34, 38.Population structure analysis showed that four subpopulations occurred in the 165 cauliflower inbred lines using the model-based method STRUCTURE. We also found that the structure analysis did not reveal a clear pattern of classification of most cauliflower inbred lines according to their curd maturity, curd solidity or geographic origins. For example, the cauliflower inbred lines derived from Zhejiang and Fujian were classified to all four subpopulations. Similar results were also observed in the cluster analysis . We alsoMaturity is routinely regarded as the most reliable index for classifying cauliflower cultivars in previous study . HoweverCurd solidity is one important trait that is usually considered by breeders in cauliflower breeding. Breeders usually assess genetic relationships using curd solidity as a simple index for classifying cauliflower germplasms. However, we also found that most cauliflower inbred lines did not cluster tightly according to their curd solidity . Thus, iCauliflower has narrow genetic variation based on some previous studies –3, 21. WThe 165 cauliflower inbred lines presented relatively narrow genetic diversity assessed by the 43 SSR markers, and were clustered into four primarily categories by cluster analysis and structure analysis. The clustering patterns of the main inbred lines were clearly not consistent with their curd maturity, curd solidity or geographic origins. Therefore, we suggest that valuable genes among different types of inbred lines be pyramided to increase genetic diversity to obtain desirable hybridization combinations in future cauliflower breeding programs.S1 Table* Maturity was expressed as the days from transplant to harvest when the inbred lines were cultivated in autumn in Zhejiang, China. Early maturity (<70d), Intermediate maturity(≥70d and<100d), Late maturity(≥100d).(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(TXT)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


F. serratus using 22 microsatellite markers. Samples were taken across latitudes and a range of temperature regimes at seven locations with decadal sampling (2000 and 2010).The spatial distribution of genetic diversity and structure has important implications for conservation as it reveals a species’ strong and weak points with regard to stability and evolutionary capacity. Temporal genetic stability is rarely tested in marine species other than commercially important fishes, but is crucial for the utility of temporal snapshots in conservation management. High and stable diversity can help to mitigate the predicted northward range shift of seaweeds under the impact of climate change. Given the key ecological role of fucoid seaweeds along rocky shores, the positive effect of genetic diversity may reach beyond the species level to stabilize the entire intertidal ecosystem along the temperate North Atlantic. In this study, we estimated the effective population size, as well as temporal changes in genetic structure and diversity of the seaweed Across latitudes, genetic structure and diversity remained stable over 5–10 generations. Stable small-scale structure enhanced regional diversity throughout the species’ range. In accordance with its biogeographic history, effective population size and diversity peaked in the species’ mid-range in Brittany (France), and declined towards its leading and trailing edge to the north and south. At the species’ southern edge, multi-locus-heterozygosity displayed a strong decline from 1999 to 2010.Fucus. Survival and adaptation of the low-diversity leading edge of F. serratus may be enhanced by regional gene flow and ‘surfing’ of favorable mutations or impaired by the accumulation of deleterious mutations. Our results have clear implications for the conservation of F. serratus at its genetically unique southern edge in Northwest Iberia, where increasing temperatures are likely the major cause for the decline not only of F. serratus, but also other intertidal and subtidal macroalgae. We expect that F. serratus will disappear from Northwest Iberia by 2100 if genetic rescue is not induced by the influx of genetic variation from Brittany.Temporally stable genetic structure over small spatial scales is a potential driver for local adaptation and species radiation in the genus The online version of this article (10.1186/s12862-018-1213-2) contains supplementary material, which is available to authorized users. Understanding temporal stability of genetic structure and diversity is crucial for the utility of temporal snapshots in conservation management and to infer how climate-induced range shifts might affect the future distribution and adaptive potential of species. In trailing edge populations, effective population size and genetic diversity are considered major keys to adaptive potential and subsequent persistence under climate change , 2. In cJaera albifrons [Zostera marina [Excirolana braziliensis [Studies that assess temporal genetic stability are rare in marine species, and mostly confined to fisheries management to ensure sustainable exploitation of economically important species –14. Whillbifrons . Howeverlbifrons –18. On ta marina  and overiliensis . These cDue to their high sensitivity to rising temperatures, responses of marine intertidal species are considered as early warning signals for the impact of climate change –25. AmonNe [Ne, and low gene flow between populations, genetic drift generally plays the predominant role, effectively neutralizing selection, and eroding genetic diversity through stochastic fixation or loss of allelic variations [Ne and temporal stability of genetic diversity patterns are particularly important for restoration and conservation efforts of fucoid seaweeds, only a single Norwegian population of F. serratus has so far been assessed [Ecological niche models, however, do not consider the species’ plastic and adaptive potential that could mitigate the predicted northward shifts. Adaptive potential depends largely on a population’s genetically effective size, Ne , or the Ne . At low riations –38. Althassessed .F. serratus is an excellent model for the study of temporal evolution and stability of genetic structure and diversity across a range of contrasting temperature regimes. It is one of the dominant intertidal seaweeds along the Northeast-Atlantic rocky shore from northern Portugal to northern Norway [2 emission scenario projections [F. serratus to climate change is expected to vary regionally, given the species’ regional patterns of genetic diversity [The canopy-forming seaweed n Norway . Arctic jections . In contjections , as tempjections . The susiversity , in combiversity .F. serratus is highest in the two former, large glacial refugia (20–18 thousand years ago (kya)) in Southwest Ireland, and Brittany [F. serratus (25 °C) [Genetic diversity of Brittany , 43. TheBrittany , 43. Cur (25 °C) , 44, inh (25 °C) –48.F. serratus decreases from its mid-range of distribution towards higher latitudes and is lowest in leading edge populations in northern Norway [Genetic diversity of n Norway , 43. LowNe is a good indicator for temporal genetic stability, its estimation relies on temporally-spaced genetic data [Ne of F. serratus has been estimated in only a single population close to Bergen (Norway) over eight years [Ne between 73 and 386 was regarded insufficient for long-term survival under environmental change [Ne and genetic diversity throughout the species’ latitudinal range of distribution cannot be inferred from a single location.While tic data . Due to ht years . The estl change . HoweverNe of F. serratus across latitude and temperature at seven locations with decadal sampling (2000 and 2010), a period in which Europe experienced three heat waves in 2003, 2006, and 2010 [Estimating climate change susceptibility in a species with low gamete/zygote dispersal requires to assess temporal genetic stability across its latitudinal and thermal range of distribution. In this study, we estimated and 2010 –52. HereF. serratus  that involved at least one of the co-authors and one or more colleagues from the country where the collection was made.Individuals were sampled ca. ten years apart from the same seven populations spanning the latitudinal distribution of tus Fig. . EthicalVariability in daily average sea surface temperatures and surface air temperatures at the sampling locations Fig. , recordeDNA was extracted from 2 mg silica dried tissue according to  with thePolymerase chain reactions (PCRs) with 5 μl total volume contained 1 μl purified DNA template, 1.34 μl nuclease-free Water , 2.5 μl of AmpliTaq Gold 360 MM  and 0.08 μl of each forward and reverse primer . PCR was performed in a Veriti 96-Well Thermal Cycler . The conditions are depicted in Additional file The fragment lengths were determined on an ABI 3500xl Genetic Analyzer from 1 μl of diluted PCR products  and multi-locus heterozygosity (MLH), the number of heterozygous loci per individual divided by the number of loci, were calculated for each sampling location. Regional estimates were obtained after pooling the two spatial samples from each of the Norwegian, Spanish and French regions. Regional estimates were not possible for the Danish region because only one population was sampled. Hexp was calculated with the R package ‘DEMEtics’ [α was estimated with the R package ‘PopGenReport’. For local estimates, α was normalized to a sample size of 24, the smallest number samples in a population. For regional estimates α was normalized to a sample size of 24, and additionally, to a sample size of 50. MLH was estimated with the R package ‘InbreedR’. Inbreeding coefficients FIS [Hexp, α, FIS, and MLH at each sampling location with Wilcoxon rank sum tests in R [Average locus heterozygosity orrected ), alleliEMEtics’ , and α wents FIS  were eststs in R . To assests in R . Additiosts in R .Ne) were estimated with an assumed generation time of 2 years [Effective population sizes  and Ne with Pearson’s product moment correlation in R [Temporal genetic changes at each sampling location and geographic genetic differentiation within and between all historical and recent samples were estimated by the fixation index FST  using GEFST  and the ST [Dest ) using tST [Dest . Dest motellites , 71. Staion in R . Additioion in R . Finallyion in R .tructure revealed clear differences between regions but not with time , and the overall historical FST value (0.21) did not differ significantly (p = 0.567) from the present-day value (0.22), indicating that spatial genetic differentiation between populations was globally consistent over time  did not differ significantly (p = 0.636) and were positively correlated . Moreover, average present-day values did not differ significantly (p > 0.05) from average historical values .Stable population diversities through time were indicated by significant correlations of historical and present-day intra-population diversity indices  exceeded local estimates (standardized to 24 samples) in all regions  was highest in the French population ‘Ile de Siec’  and lowest in the Norwegian population ‘Grense Jakobselv’ (Ne = 62) . The temporal decrease in MLH in Ribadeo2 was strong but not significant  Fig. . Ne for F. serratus [Fucus vesiculosus [Z. marina, genotypic diversity not only enhanced biomass production, but also abundance of the associated fauna under near-lethal water temperatures [F. serratus is also expected to be important for conservation and management of the entire intertidal ecosystem along temperate rocky shores. Across the latitudinal range of F. serratus, genetic diversity and differentiation remained stable for 5–10 generations at regional scales, and in all but the Norwegian region at local spatial scales  has a wide confidence interval as compared with the other populations  in the ‘Kirkenes’ population in 2010. Thus, regional gene flow may uncouple Ne from genetic stochasticity effects at the species’ Northern edge of distribution.An ariation , 87. Howl change . Stable Ne [F. serratus less sensitive to genetic drift [As expected for neutral loci, genetic diversity was positively related with Ne , 38. Botic drift , 37. A dic drift .F. serratus to adapt to Arctic shores [Low genetic diversity does not necessarily lower the evolutionary potential of c shores . The evoc shores  may decrc shores , 9. On tc shores , 5, 90. F. serratus at its southern edge. Reductions in MLH from 1999 to 2010 were close to significant , although, Hexp, α, and FIS remained temporally stable. This agrees with stability of Hexp and α over 7–9 years in fragmented southern edge populations of the kelp species Laminaria digitata [MLH might be explained by increased selection pressure for heat-tolerance, although there is only indirect experimental evidence for this. Acclimation potential to further thermal stress is likely impeded in this population by chronically high expression of heat shock protein genes [Our results have clear implications for the conservation of digitata , and is digitata . In othein genes , 41, 93.in genes . AlthougF. serratus may be high [Zostera marina [Laminaria digitata [The value of conserving the southern edge of  be high . Because be high . The cona marina , 95. We digitata  can be eF. serratus populations spanning its latitudinal range reliably reflect patterns across local and regional spatial scales and across various thermal backgrounds for at least one decade. Stable small-scale structure enhances regional genetic diversity throughout the species’ range of distribution and is a potential driver for local adaptation [Temporal snapshots of genetic diversity and structure in aptation  that mayaptation –98.MLH appears to be the most stress-sensitive measure of diversity, displaying a strong decline at the species’ southern edge of distribution. As sandy warm shores separate the Iberian southern edge from the genetically diverse Brittany region, genetic rescue by the influx of genetic variation [ariation , 88 mighF. serratus, but also other intertidal and subtidal macroalgae in Northwest Iberia [F. serratus [F. serratus, may largely disappear from southern edges but retain potential to persist in small subtidal bottleneck populations in cool upwelling regions [Increasing temperatures are likely the major cause for the decline not only of t Iberia , 99–101,t Iberia . Kelp spt Iberia . Accordiserratus . Howeverserratus , 46) as serratus , 106. We regions .Additional file 1:Surface air temperatures. Daily average surface air temperatures (SAT) at each of the seven sampling sites from 1999 to 2011 with gaps in year 2006 for the French and Spanish sampling sites. SATs were identical between the two Norwegian sampling sites as well as between the two French and the two Spanish sampling sites. (PDF 513 kb)Additional file 2:Microsatellite markers. Characteristics of each microsatellite marker, including cycling conditions and multiplexing. (XLSX 8 kb)Additional file 3:PCR cycling protocols. Time-release (a) and no-time-release (b) PCR cycling protocols. In the time-release protocol, the heat-activated DNA-polymerase was progressively released during the thermal cycling process. Annealing temperatures and number of cycles indicated with an X are specified for each marker in Additional file Additional file 4:tructure format. The first row contains the names of all 31 markers. The following rows contain the individual genotype data. Each individual is represented in 2 consecutive rows. The first column contains the name of the individual, the second row contains the population number that individual belong to. The following 31 columns show the alleles of each marker as microsatellites base pair lengths. The population numbers (1–14) refer to the following sampling locations and times: 1) Gjerild Klint, present-day; 2) Gjerild Klint, historical; 3) Green Top, present-day; 4) Green Top, historical; 5) Ile de Siec, present-day; 6) Ile de Siec, historical; 7) Grense Jakobselv, present-day; 8) Grense Jakobselv, historical; 9) Kirkenes, present-day; 10) Kirkenes, historical; 11) Ribadeo 1, present-day; 12) Ribadeo 1, historical; 13) Ribadeo 2, present-day; 14) Ribadeo 2, historical. (TXT 195 kb)Microsatellite raw data. Microsatellite genotypes in SAdditional file 5tructure format. The first row contains the names of all 31 markers. The following rows contain the individual genotype data. Each individual is represented in 2 consecutive rows. The first column contains the name of the individual, the second row contains the population number that individual belong to. The following 31 columns show the alleles of each marker as microsatellites base pair lengths. The population numbers (1–14) refer to the same sampling locations and times as in Additional file Corrected microsatellite data. Microsatellite data corrected for allelic dropout in Smbers 1–1 refer toAdditional file 6:FST and Dest with p values. (XLSX 10 kb)Spatial differentiation. Regional and local genetic differentiation between sampling sites in historical and present samples estimated by Additional file 7:Ne), temporal genetic differentiation (FST and Dest), and p values for temporal changes in diversity measures at each sampling site. (XLSX 6 kb)Temporal changes. Estimates of effective population size , allelic richness (α), and multi-locus heterozygosity (MLH), for each location and region with standard errors, and inbreeding coefficients FIS with p values for each population. (XLSX 8 kb)Diversity estimates. Diversity estimates, including heterozygosity  for each population and sampling time point . (XLSX 6 kb)Temporal versus local genetic differentiation. Temporal genetic change in comparison to local and regional genetic differentiation(


Akabane disease (AD), a barrier to international trade for endemic areas with far economic impact on the countries, is caused by Akabane virus (AKAV). Commercial enzyme-linked immunosorbent assay (ELISA) is a commonly used diagnostic technique for AKAV infection, including the IDEXX and IDVET ELISA kits. However, the comparative evaluation of the IDEXX and IDVET ELISA kits has not been published. The object of this study was to evaluate the test performance of the two commercial ELISA kits in detecting serum anti-AKAV antibodies in cattle.With virus neutralization test (VNT) as the “relative gold standard”, the diagnostic sensitivity (DSe) was 80.39% (123/153) and 93.46% (143/153) for the IDEXX and IDVET ELISA kit, when suspect samples were included. The diagnostic specificity (DSp) for the IDEXX and IDVET ELISA kit was 93.48% (502/537) and 82.31% (442/537), respectively.Both of the tested ELISA kits could be applied to detect antibodies against AKAV in cattle serum. The IDVET ELISA kit had a higher DSe. The IDEXX ELISA kit possessed the higher DSp. These results have important implications if the kits are used to screen herds or individual cattle in surveillance programs, or at border crossings for import-export inspection and quarantine. Akabane disease (AD), characterized by abortions, stillbirth and congenital defects in pregnant ruminants, is caused by AKAV which was first identified by virus isolation in Japan . NowadayPeribunyaviridae   × 100). Similar to the IDEXX ELISA kit, a “grey zone” also existing for data interpretation . An S/N value of < 40% was considered positive in our study.The diluted sera from 1/1 to 1/256 and all 690 sera were tested for the presence of anti-AKAV antibodies using the IDVET ELISA kit . All sera were screened for antibodies against the AKAV strictly according to the manufacturer’s instructions. The competition percentage (S/o-Pe)/(1- Pe), Po = (TP+ TN)/ (TP + FN + TN + FP), Pe = [TP + FN] × [TP + FP] + [TN + FP] × [TN + FN])/ (TP + FN + TN + FP)2. TP = number of true positives, TN = number of true negatives, FP = number of false positives and FN = number of false negatives. The κ value was calculated to test the level of agreement between the ELISA tests and VNT. The interpretation of agreement was: ≤0 = poor, 0.01–0.2 = slight, 0.21–0.4 = fair, 0.41–0.60 = moderate, 0.61–0.80 = substantial and 0.81–1 = almost perfect ) × 100, DSp = (TN/ [TN + FP]) × 100, κ value = . Statistical analyses were performed using the analytical software package SPSS Statistics 23  .


Drosophila T4/T5 neurons, a class of closely related neuronal subtypes with different wiring patterns. Eight subtypes of T4/T5 neurons are defined by combinations of two patterns of dendritic inputs and four patterns of axonal outputs. Single-cell profiling during development revealed distinct transcriptional programs defining each dendrite and axon wiring pattern. These programs were defined by the expression of a few transcription factors and different combinations of cell surface proteins. Gain and loss of function studies provide evidence for independent control of different wiring features. We propose that modular transcriptional programs for distinct wiring features are assembled in different combinations to generate diverse patterns of neuronal connectivity.Patterns of synaptic connectivity are remarkably precise and complex. Single-cell RNA sequencing has revealed a vast transcriptional diversity of neurons. Nevertheless, a clear logic underlying the transcriptional control of neuronal connectivity has yet to emerge. Here, we focused on  Brain function relies on precise patterns of synaptic connections between neurons. At the cellular level, this entails each neuron adopting a specific wiring pattern, the combination of specific synaptic inputs and outputs. In invertebrates, stereotypical wiring patterns are genetically encoded in the programs regulating the development of neurons. Much of the specificity of inputs and outputs of neurons in the mammalian CNS is also genetically determined .Vast numbers of neurites from a diversity of neurons are intermingled within the developing central nervous system, and they form highly specific synaptic connections with a discrete subset of the neurons they contact. Studies in both vertebrates and invertebrates have led to the identification of cell surface proteins (CSPs) that mediate selective association between neurites . Gain anDrosophila visual motion detection pathway. We envision that our findings in this system will provide insights into the genetic logic of wiring specificity more broadly in both vertebrate and invertebrate systems.Single-cell RNA sequencing (RNA-Seq) provides an unsupervised approach to uncover the genetic programs underlying specific wiring features by exploring subtype-specific transcriptomes during development. As neuronal subtypes exhibit differences in characteristics other than wiring patterns, the relationship between genes and wiring specificity may be obscured by genes contributing to other aspects of neuronal diversity. Therefore, sets of closely related neurons with highly specific differences in wiring patterns are ideally suited to uncover the genetic programs specific to wiring. Here, we explore the genetic logic underlying synaptic specificity in one such set of neurons: T4/T5 neurons of the Drosophila brain revealed a common transcriptional signature for all T4/T5 neurons, genetic programs for individual subtypes have not been identified . This developmental time point precedes a period of widespread synaptogenesis in the visual system, and coincides with the appearance of four discrete synaptic layers  in the LoP neuropil. Neurons were purified from dissected optic lobes by FACS using a transgenic line with nuclear GFP selectively expressed in all T4/T5 neurons . RNA-SeqWe applied independent component analysis (ICA) followed by a graph-based clustering method to separate transcriptionally distinct cell populations . UnsuperTo identify genes preferentially expressed in T4/T5 subtypes, we performed differential gene expression analysis between each of the eight individual clusters and all other cells in the dataset . This revealed 69 genes which were strongly expressed in some clusters and not in others. Cluster-enriched genes, however, were not specific to single clusters. By contrast, for instance, each of the five subtypes of lamina neurons is defined by at least one subtype-type specific transcription factor . Thus, wThe absence of unique markers for individual subtypes suggested that they were instead defined by unique combinations of genes. ICA has been shown to capture groups of genes corresponding to discrete biological phenomena . Intrigubifid (bi) defined the two groups of clusters separated by Axis 1, grain (grn) defined the clusters separated by Axis 2, and TfAP-2 defined the clusters separated by Axis 3  driven by a group of genes differentially expressed along each axis. Many of these genes were expressed in a binary (ON/OFF) pattern in one of the two groups of clusters separated along each axis . A smally Axis 3 . Thus, wWe next sought to map transcriptional clusters to T4/T5 subtypes, and to determine the biological significance of the observed axes of transcriptional diversity. We inspected in vivo expression patterns of genes associated with the three primary axes of diversity using transgenic reporters inserted into the endogenous loci .Fasciclin 2 (Fas2) and klingon (klg), respectively ) and c/d ) )  ) . Thus, An (Con)) . This cobeat-IV (an Ig superfamily protein) and the TF grn. Both genes were expressed in LoP layers b/c, but not a/d. Thus, Axis 2 further separated subtypes into inner (b and c) and outer (a and d) LoP layer subtypes in a symmetrical fashion, defining specificity of axonal outputs between adjacent layers within the two broad domains of the LoP  and performed comprehensive pairwise comparisons with each of the other subtypes (i.e. ‘one versus one’).First, we compared T4a and each subtype that differed by a single wiring feature: either axonal outputs , or dendritic inputs (T5a) , were also expressed in groups of subtypes sharing either axonal outputs or dendritic inputs .In addition to three primary axes of transcriptional diversity, this analysis shows that a number of DEGs exhibited more distinct LoP layer-specific patterns. For example, many DEGs were specifically expressed or suppressed in T4/T5 subtypes from a single LoP layer .Many of the DEGs have been implicated in neuronal wiring specificity . ApproxiTaken together, these results reveal that the transcriptional organization of T4/T5 neurons mirrored their wiring patterns. Discrete groups of co-regulated genes reiteratively defined either shared axon or shared dendrite wiring patterns among different subtypes. These groups of genes were assembled in different combinations to uniquely define the eight T4/T5 subtypes.To evaluate how gene expression in T4/T5 subtypes changes during development, we profiled T4/T5s at an earlier time point, 24 hr APF. Similar to our dataset at 48 hr APF, we identified eight distinct populations separated by three equivalent axes of transcriptional diversity. Many of the same genes were associated with these axes at both time points, allowing us to match subtypes between 24 hr and 48 hr APF .bi, grn, TfAP-2) were expressed in the same sets of subtypes at both time points, suggesting they may contribute to stable subtype identities during development . These data indicate that similar transcriptional programs unfold in parallel among T4/T5 subtypes with shared wiring features.Interestingly, dynamic changes in gene expression were also coordinated among subtypes with shared wiring features . For exadac and bi/pros separated a/b (dac+) from c/d (bi+/pros+) subtypes  and outer  layer subtypes in a symmetrical fashion (i.e. Axis 2). This suggested that two levels of transcriptional regulation, acting either sequentially or in a temporally overlapping way, control development of four types of T4/T5 axonal outputs.A remarkable correspondence between transcriptional programs and wiring patterns suggested that these programs control development of corresponding features of T4/T5 neurons. During the period covered in our study (24–48 hr APF) four LoP layers form in two discrete lamination steps . The T4/bi specifies c/d subtypes and formation of corresponding LoP layers. RNAi of bi in all T4/T5 neurons results in loss of the c/d domain of the LoP, whereas overexpression results in loss of the a/b domain. In both cases, further sublamination of remaining inner and outer LoP layer pairs still occurs , both domains failed to sublaminate into inner and outer layers, instead forming a single layer each  expression patterns of two TFs, Drosophila visual system, which differ by specific variations in wiring, with the expectation that transcriptional differences among them would reflect the specificity of dendritic inputs and axonal outputs. A unique attribute of T4/T5 neurons is that the same dendritic and axonal wiring patterns are reiteratively used among different subtypes; each neuron can be described by a unique combination of one of four types of axonal outputs and one of two types of dendritic inputs. We anticipated that this property of T4/T5 neurons would provide an opportunity to assess the relationship between specific genetic programs and fundamental features of neuronal architecture.Single-cell transcriptional profiling has the potential to transform our understanding of the genetic programs controlling wiring in complex nervous systems . HoweverUnsupervised analysis revealed that separable transcriptional programs correlate with these specific wiring features. We demonstrate through gain and loss of function experiments that these programs control specific axonal targeting features, which are separable from other features (e.g. dendrite targeting). These programs can be re-assembled in a modular fashion to generate neuronal subtypes with different combinations of wiring features. A modular transcriptional architecture may provide a general strategy for discrete modifications to neuronal connectivity in development and evolution.Lim1, Drgx, acj6)  . Perturbx, acj6) . We findx, acj6) .bi is required for lamination of the broad a/b from c/d LoP domains  were defined by binary expression of TfAP-2. Arborization of dendrites in M10 (T4) or Lo1 (T5) occurs during initial neurite guidance steps, preceding the developmental stages covered in this study  each target one of four LoP layers, a-d. The ultimate layered architecture of neuropils develops through sequential lamination into increasing numbers of layers . Togethe domains , whereasis study . We hypois study .bi- and bi+ progenitor populations. Neuroblasts from each population undergo two terminal Notch-dependent asymmetric divisions to give rise to the eight subtypes  were reared at 25°C on standard medium. For developmental analysis by immunohistochemistry, sorting, and sequencing, white pre-pupae (0 hr APF) were collected and incubated for indicated number of hours.Flies , 10XUAS-IVS-myr::tdTomato (Bloomington Drosophila Stock Center (BDSC #32222)), 23G12-Gal4 (BDSC #49044), {R59E08-p65ADZp (attP40); R42F06-ZpGdbd (attP2)} , UAS-H2A::GFP , UAS-CD4-tdGFP (BDSC #35839), 23G12-LexA (BDSC #65044), LexAop-myr::tdTomato (Zipursky laboratory), 10XUAS-myr::GFP (Zipursky laboratory), 10XUAS-IVS-mCD8::RFP (BDSC #32219), Mi{PT-GFSTF.1}klg[MI02135-GFSTF.1] (BDSC #59787), Mi{PT-GFSTF.1}beat-IV[MI05715-GFSTF.1] (BDSC #66506), dpr2-Gal4 (Zipursky laboratory), P{w[+mW.hs]=GawB}grn[05930-GAL4] (BDSC #42224), Mi{y[+mDint2]=MIC}beat-VI[MI13252] (BDSC #58680), P{y[+t7.7] v[+t1.8]=TRiP.HMS01085}attP2 (UAS-grn-RNAi) BDSC #33746), UAS-grn.ORF.3xHA (FlyORF #F001916), 42F06-Gal4 (BDSC #41253), UAS-p35 (BDSC #5072).klg, beat-IV, dpr2, beat-VI, or to w[1] males for Fas2 immunolabeling. For visualization of grn-expressing neurons, grn-Gal4 was used to drive 10XUAS-myr::GFP. For grn phenotypes, virgin females {w[1];UAS-CD4-tdGFP;23G12-Gal4} were crossed to males with UAS-grn-RNAi or UAS-grn.ORF.3xHA transgenes. For p35 rescue experiments, virgin females with 23G12-Gal4, UAS-CD4-tdGFP and with or without UAS-grn.ORF.3xHA were crossed to males with 42F06-Gal4, and with or without UAS-p35 transgene, as indicated. All RNAi and overexpression crosses were raised at 29°C.For visualization of T4a clone, virgin females {pBPhsFlp2::PEST; 10XUAS-IVS-myr::tdTomato; UAS-FSF-smGdP::HA_V5_FLAG/CyO::TM6B} were crossed to males with the T4/T5-specific Split-Gal4 driver {R59E08-p65ADZp (attP40); R42F06-ZpGdbd (attP2)} (JRC_SS00324). White pre-pupae were heat shocked at 37°C for 3 min. For FACS sorting of GFP+ T4/T5 neurons, 23G12-Gal4 was used to drive UAS-H2A::GFP. For T4/T5 developmental timecourse, 23G12-Gal4 was used to drive UAS-H2A::GFP and 10XUAS-IVS-mCD8:RFP. For visualization of all T4/T5 neurons with subtype-specific markers, female virgins of the genotypes: {23G12-LexA; LexAop-myr::tdTomato; 10XUAS-myr::GFP/CyO::TM6B} or {w; 10xUAS-IVS-mCD8::RFP; 23G12-Gal4} were crossed to males with MiMICs or their derivatives for Drosophila Medium (Gibco #21720–024), and fixed in PBS (Bioland Scientific LLC #PBS01-03) containing 4% paraformaldehyde  for 25 min at room temperature (RT). Brains were rinsed repeatedly with PBST (PBS containing 0.5% Triton-X100 (Sigma #T9284)), and incubated in blocking solution ) for at least 1 hr at RT prior to incubation with antibody. Brains were incubated sequentially with primary and secondary antibodies diluted in blocking solution overnight at 4C, with at least 2 PBST rinses followed by 2 hr incubations at RT in between and afterwards. Brains were transferred to 50% (for 30 min), then 100% EverBrite mounting medium (Biotium #23001) and mounted on slides for confocal microscopy.Brains were dissected in ice-cold Schneider’s Primary antibodies and dilutions used in this study were as follows: chicken anti-GFP , rabbit anti-dsRed , mouse anti-Brp , 1:30), mouse anti-Fasciclin II , mouse anti-V5 , rabbit anti-Dcp-1 . Secondary antibodies and dilutions used in this study were as follows: goat anti-chicken Alexa Fluor 488 (AF488) , goat anti-mouse AF488 , goat anti-rabbit AF568 , goat anti-rat 568 , goat anti-rabbit AF647 , and donkey anti-mouse Cy5 .Immunofluorescence images were acquired using a Zeiss LSM 880 confocal microscope with Zen digital imaging software. Optical sections or maximum intensity projections were level-adjusted, cropped and exported for presentation using Image J software (Fiji). Reported expression patterns were reproducible across three or more biological samples. For cell number quantifications, optic lobes were mounted with ventral side facing objective as in , and a sMales with 23G12-Gal4 driver were crossed to virgin females with UAS-H2A::GFP reporter. F1-generation female white pre-pupae were collected at 0 hr APF and reared at 25°C. Optic lobes were dissected out at 24 hr and 48 hr APF from 27 and 18 pupae, respectively. Brain tissue was incubated in papain (Worthington #LK003178) and Liberase protease (Sigma-Aldrich #5401119001) cocktail at 25°C for 15 min. Next, tissue was gently washed twice with 1X PBS and dissociated mechanically by pipetting. Cell suspension was filtered through 20 μm cell-strainer (Corning #352235). Single-cell suspension was FACS sorted (BD FACSAria II) to isolate GFP-positive cells.FACS-sorted single-cells were captured from a cell suspension using the 10X Chromium platform (~6500–7000 cells loaded). Single-cell RNA-Seq libraries were generated using Chromium Single Cell Reagent Kit V2 according to the manufacturer's protocol, with 12 cycles of PCR for cDNA amplification. RNA-Seq libraries were sequenced using Illumina Hiseq 4000 platform (paired-end 100 bp reads). Each sample was captured and sequenced using one lane of 10X Chromium and one lane of HiSeq 4000.Drosophila melanogaster genome sequence and gene annotations from FlyBase (release 6.22). Both samples were sequenced at mean depth of 92,000 reads per cell (92% saturation). Average fractions of reads uniquely (confidently) mapped to genome and transcriptome were 93% and 83%, respectively.Raw Illumina base call files (*.bcl files) were converted into fastq files using bcl2fastq . Fastq files were processed using Cell Ranger (2.2.0) pipeline with default parameters. Reference transcriptome package for Cell Ranger was generated using All steps of single-cell data analysis were performed using functions and methods implemented in Seurat package (2.3.4) . AnalysiFor 24 hr dataset, we recovered 3833 cells (median of 1447 genes and 3353 transcripts per cell). For 48 hr dataset, we recovered 3894 cells (median of 1633 genes and 4389 transcripts per cell). Initial set of cells was pre-filtered based on total number of detected genes , and percentage of mitochondrial transcripts (max. 5%). After pre-filtering, 3312 and 3620 cells remained for 24 hr and 48 hr datasets, respectively. Raw transcript counts were log-normalized using NormalizeData function. Next, we regressed out total number of transcripts per cell (nUMI) and scaled expression values to Z-scores using ScaleData function.Sets of highly variable genes were selected using FindVariableGenes function . Highly variable genes were used to perform independent component analysis (ICA) using RunICA function. Independent components (ICs) were manually inspected to identify and flag outlier cells. In total, 241 and 60 cells were flagged as outliers in 24 hr and 48 hr datasets, respectively. Outlier cells were removed from subsequent steps of the analysis. After quality control and filtering, final datasets included 3071 cells for 24 hr, and 3557 cells for 48 hr datasets.We repeated selection of highly variable genes on final datasets using the same parameters (2290 genes), and used them to perform ICA. Inspection of results of ICA revealed that the three ICs separated cells into two discrete populations of approximate halves. Final clustering was performed based on these 3 ICs using the graph based clustering approach implemented in FindClusters function with default parameters. In addition to ICA, we performed principal component analysis (PCA) using the same set of highly variable genes. Comparison of ICA and PCA results revealed robustness of clusters identified by both methods .t-distributed stochastic neighbor embedding (tSNE) was used to visualize cellular heterogeneity based on ICA and PCA results using RunTSNE function (perplexity: 100). Clusters were validated and matched to eight morphological T4/T5 subtypes using in vivo expression patterns of marker genes .Similar to 48 hr dataset, we selected highly variable genes (2198 genes), and used them to perform ICA. We selected three ICs that were driven by similar sets of genes as ICs used for clustering of 48 hr dataset. Selected ICs were used to perform clustering and tSNE using same parameters as 48 hr dataset. Cluster identities were matched with T4/T5 subtypes using expression patterns of the same sets of marker genes . In compDifferentially expressed genes (DEGs) were identified using Wilcoxon rank-sum test implemented in FindMarkers function . We performed this analysis for each cluster against all other cells in dataset , single-cell expression matrix and cell clustering results were deposited to NCBI Gene Expression Omnibus (GEO) under accession: GSE126139. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.Acceptance summary:This manuscript addresses the central issue of how transcription factor (TF) codes dictate neuronal sub-type cell fate, with particular emphasis upon how the proper mix of cell surface proteins (CSPs) are activated in each sub-type. Ultimately, this work informs regarding the genetic logic underlying the regulation of neuronal connectivity, addressed using single-cell sequencing of a group of eight closely related neurons with different wiring specificity.Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Eve Marder as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Bing Ye (Reviewer #1).Thank you for submitting your article "Modular transcriptional programs separately define axon and dendrite connectivity" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission. Please aim to submit the revised version within two months.Summary:Drosophila visual system, and perform scRNA-seq upon FACS-purified cells. They find that T4/T5 neurons belong to eight major sub-types, and identify TF and CSP signatures for these groups. They functionally address the role of one TF, grn, and find that it controls a particular feature of the sub-types expressing this TF. Another TF with sub-type defining expression, bi, was addressed in a previously published study. The authors propose a model whereby TF codes, rather than single TFs, govern CSP codes, to thereby govern neuronal morphology. This general notion has been around for quite some time, but detailed examples of precise players and pathways have been frustratingly slow to emerge. Hence, progress in this area is of great importance to the field of developmental neurobiology.This manuscript addresses the central issue of how transcription factor (TF) codes dictate neuronal sub-type cell fate, with particular emphasis upon how the proper mix of cell surface proteins (CSPs) are activated in each sub-type. They focus upon the T4/T5 neurons of the Major Concerns:grn and TfAP-2, and briefly mention some others, but what are the complete TFs codes for each sub-type? Most of the data depict a mix of DEG, CSPs and TFs. Can the authors address this issue in greater depth?1) It would be valuable to separately show what the TF and CSP codes are for each of the eight sub-types. More specifically, it is unclear to this reviewer what the TF codes are for the eight sub-types of T4/T5 neurons. They discuss bi, Minor Concerns:1) Can the authors also conduct RNAi of TfAP-2 and/or other TFs that they believe may specify T4/T5 sub-types to confirm some aspect of functional significance. This would be a substantive addition to the manuscript, elevating significance and impact, and they authors may likely have some of this data already in place?grn LOF and GOF phenotype, and the previously published bi phenotype, fits with a combinatorial model for T4/T5 sub-type cell fate specification. Could the authors discuss this issue?2) It is difficult to understand if the grn overexpression, and could observe a similar phenotype even with apoptosis suppressed. Can they use the same approach to suppress apoptosis in grn RNAi, to see if this phenotype is also independent of apoptosis?4) Figure 6: They use UAS-p35 to suppress the apoptosis triggered by 5) When describing the T4/T5 neurons maybe they could elaborate more regarding the numbers of these neurons, and previous predictions of sub-types, based upon gene expression and morphology.6) How does the 24h APF and 48h APF time-point used for scRNA-seq relate to when the T4/T5 neurons are born? Major Concerns:1) It would be valuable to separately show what the TF and CSP codes are for each of the eight sub-types. More specifically, it is unclear to this reviewer what the TF codes are for the eight sub-types of T4/T5 neurons. They discuss bi, grn and TfAP-2, and briefly mention some others, but what are the complete TFs codes for each sub-type? Most of the data depict a mix of DEG, CSPs and TFs. Can the authors address this issue in greater depth?Drosophila brain , or have been shown to regulate common T4/T5 morphology . We show that these TFs represent a common regulatory program for all T4/T5 neurons. Modular assembly of feature-specific TF patterns overlying this common T4/T5 program generates each T4/T5 subtype. These TF codes are stable and define each subtype at both 24h and 48h APF .We agree that the textual summary of our findings in the Discussion does not do a good job of describing the modular organization of T4/T5 transcriptional programs we have uncovered in this analysis . We have expanded the description and most importantly included three new figures  to emphasize this modularity. We also include expression patterns of TFs that were shown to distinguish T4/T5s from other neurons in CSP patterns expressed by each subtype are more complex and dynamic than the pattern of transcription factors. We now include a supplementary figure with summary of expression patterns of all subtype-enriched CSPs with cell adhesion domains at both 24h and 48h APF . As with the TFs, the combinations of CSPs are also largely expressed in modular fashion across subtypes with shared wiring features. While the contents of Figure 7 and its supplementary figures partially overlap with data presented in other figures, we believe this overlap will provide a clearer summary of modular transcriptional program we have uncovered.Minor Concerns:1) Can the authors also conduct RNAi of TfAP-2 and/or other TFs that they believe may specify T4/T5 sub-types to confirm some aspect of functional significance. This would be a substantive addition to the manuscript, elevating significance and impact, and they authors may likely have some of this data already in place?bi, grn, and TfAP-2. Here we have shown that grn completes a modular TF code that defines and directs axon targeting to all four LoP layers. We have also performed RNAi of the only remaining TF, TfAP-2, with multiple lines and did not observe significant knockdown of the protein (or targeting defects). Therefore, the role of TfAP-2 in T4/T5 neurons currently remains inconclusive, and hence was omitted from our article. We have also found that removing acj6, a transcription factor common to all eight T4/T5 neurons, leads to massive disruption in the wiring patterns, including most importantly a complete loss of the four layers of axon terminals in the lobula plate. As subtype specification is the message in this paper, we do not think it is relevant to include these data.A small number of TFs can define all eight individual T4/T5 subtypes: 2) It is difficult to understand if the grn LOF and GOF phenotype, and the previously published bi phenotype, fits with a combinatorial model for T4/T5 sub-type cell fate specification. Could the authors discuss this issue?In this study we were able to dissect the combinatorial TF code into modular regulatory programs, and show how separate TFs can independently control distinct features of neural identity. Through unsupervised analysis, we were able to identify and link separate TFs to specific features of T4/T5 morphology . We have now expanded on how our phenotypes reflect modular control of wiring features in the Discussion.grn in axon targeting, supporting our hypothesis that separable transcriptional modules control discrete wiring features in T4/T5 subtypes, the primary conclusion of our paper. Here grn controls the development of “b/c”-subtype targeting, independently from axon targeting to the broad a/b and c/d LoP domains, and from dendrite targeting to M10 and Lo1. Both loss and gain of function grn reduce the number of targeting layer specificities from four to two in a predictable way. Together with earlier work on bi, which controls targeting to a/b versus c/d LoP domains , we have thus defined a complete modular TF code for T4/T5 axon targeting.Our results demonstrate a highly specific role of 4) Figure 6: They use UAS-p35 to suppress the apoptosis triggered by grn overexpression, and could observe a similar phenotype even with apoptosis suppressed. Can they use the same approach to suppress apoptosis in grn RNAi, to see if this phenotype is also independent of apoptosis?We agree this would be an excellent addition to our experiments. These experiments rely on multiple transgenes. While the location of the transgenes was compatible with the rescue of the death seen in the gain of function phenotype, it is not compatible with the loss of function. In short, three transgenes required for this experiment are inserted into the same chromosomal site in an autosome. While this is compatible for experiments requiring two of these (i.e. as it is for the rescue of the gain of function), it is not for the rescue of the RNAi loss of function.5) When describing the T4/T5 neurons maybe they could elaborate more regarding the numbers of these neurons, and previous predictions of sub-types, based upon gene expression and morphology.We have elaborated on numbers of T4/T5 neurons and references to previous sequencing efforts. In these studies, separate transcriptional programs for the eight individual morphological subtypes were not identified.6) How does the 24h APF and 48h APF time-point used for scRNA-seq relate to when the T4/T5 neurons are born?Neurogenesis of T4/T5 neurons occur early in late larval and early pupal stages of development . By 24h APF all T4 and T5 neurons have already acquired their general morphologies, with incipient dendrites in medulla layer M10 and Lobula layer 1, respectively, and T4/T5 axon terminals in the LoP neuropil. At this stage, two layers are visible in the lobula. By 48h APF axon terminals sublaminate into four layers in LoP  and the dendritic arbors are more complex. It is important to note that early stages of T4/T5 neurogenesis occurs as a wave giving rise to a gradient of neuronal maturation, similar to the generation of photoreceptors. At later stages, at least by 48h APF, development of T4 and T5 neurons appears to become synchronized in development.


Reconquista.The Iberian Peninsula is linguistically diverse and has a complex demographic history, including a centuries-long period of Muslim rule. Here, we study the fine-scale genetic structure of its population, and the genetic impacts of historical events, leveraging powerful, haplotype-based statistical methods to analyse 1413 individuals from across Spain. We detect extensive fine-scale population structure at extremely fine scales (below 10 Km) in some regions, including Galicia. We identify a major east-west axis of genetic differentiation, and evidence of historical north to south population movement. We find regionally varying fractions of north-west African ancestry (0–11%) in modern-day Iberians, related to an admixture event involving European-like and north-west African-like source populations. We date this event to 860–1120 CE, implying greater genetic impacts in the early half of Muslim rule in Iberia. Together, our results indicate clear genetic impacts of population movements associated with both the Muslim conquest and the subsequent  The Iberian Peninsula has a complex history. Here, the authors analyse the genetic structure of the modern Iberian population at fine scale, revealing historical population movements associated with the time of Muslim rule. Recently there has been an increasing focus on studying genetic differentiation at fine geographic scales, such as within countries8. Identifying such structure allows the study of recent population history, and identifies the potential for confounding in association studies, particularly when testing rare, often recently arisen variants9. The Iberian Peninsula is linguistically diverse, has a complex demographic history, and is unusual among European regions in having a centuries-long period of Muslim rule10.Genetic differentiation within or between human populations (population structure) has been studied using a variety of approaches over many years13 or only a few regions of Spain15, and typically compare groups of individuals defined a priori using broad ethnic or geographic labels, such as autonomous community. Using such approaches only limited population structure within Iberia has been identified19. Some structure within northern Spain has been detected, including statistically significant differences in frequencies of Y-chromosome haplotypes and other genetic markers between the Basque-speaking regions and other parts of Iberia12, a result consistent with a European-wide analysis using autosomal DNA20. Studies of Spain that used genome-wide data did not leverage information in correlations between genetic markers15, excepting one study21, which detected a cline of variation broadly distinguishing samples in País Vasco from other parts of northern Spain, especially Galicia, but no evidence of sub-structure in central or southern Spain. Thus the overall pattern of population structure within Spain—including subtle structure at fine geographic scales—remains uncharacterized.Previous studies of population structure in Spain have examined either a small fraction of the genome22. Previous genetic studies have reported signals of admixture from sub-Saharan Africa and/or north Africa into Iberia at some point in the past27. However, estimates of the timing of this admixture vary greatly, from as long as 74 generations ago (~100 BC)23 to 23 generations ago (~1330 CE)25. Estimates of overall mean proportions of African-like DNA in the Iberian Peninsula also vary, ranging from 2.424 to 10.6%11. Differences within Iberia have also been reported26, based on comparisons between sampled regions, with higher fractions observed in western regions of Iberia (e.g. 21.7% in Northwest Castile11) and lower fractions in the north-east . Estimates of the timing and extent of admixture tend to vary depending on the reference populations assumed to represent the ancestral mixing groups (e.g. Moroccan11 or Saharawi26), as well as heterogeneity in the ancestral make-up of the modern-day Iberian samples used in the analysis.The cultural and linguistic impact of Muslim rule in Iberia is well-documented, but the historical record is limited in its ability to inform about the extent, timing and geographic spread of genetic mixing between immigrants and indigenous Iberians over several centuries after the initial conquestReconquista.Here we analyse genome-wide genotyping array data for 1413 Spanish individuals sampled from across Spain. By using powerful, haplotype-based statistical methods we identify extensive fine-scale structure down to scales <10 km in some places. We identify a major axis of genetic differentiation that runs from east to west across Iberia. In contrast, we observe remarkable genetic similarity in the north–south direction, and evidence of historical north–south population movement. Finally, we sought to clarify the timing and composition of African-like and potentially non-African genetic contributions to the Iberian Peninsula, by jointly analysing genotype data sourced from a wide range of African and European regions. We show that modern Spanish people have regionally varying fractions of ancestry from a group most similar to modern north-west Africans. This African ancestry, identified without making particular prior assumptions about source populations, results from an admixture event that we date to 860–1120 CE, corresponding to the early half of Muslim rule. Our results indicate that it is possible to discern clear genetic impacts of the Muslim conquest and population movements associated with the subsequent 28 to these data to infer clusters of individuals with similar patterns of shared ancestry (Methods). fineSTRUCTURE inferred 145 distinct clusters, along with a hierarchical tree describing relationships between the clusters  after quality control (Methods). We applied fineSTRUCTUREers Fig. ; Methods29 present in the Iberian Peninsula around 1300 CE , even after accounting for both physical distance and autonomous community membership (Supplementary Note p = 0.12).These results reveal patterns of rich fine-scale population structure in Spain. At the coarsest level of genetic differentiation (i.e. two clusters at the top of the hierarchy) individuals located in a small region in south-west Galicia are separated from those in the rest of Spain. The next level separates individuals located primarily in the Basque regions in the north (País Vasco and Navarra) from the rest of Spain. Further down the tree  remain largely intact at the bottom of the hierarchical tree Fig.  many of p < 0.02; Fig. To further understand the relationships between the clusters inferred by fineSTRUCTURE, we examined patterns in the matrix of ancestry sharing (coancestry) between each pair of 1413 individuals Fig. . In gene30 and sub-Saharan Africa31 (Methods). We used fineSTRUCTURE to identify 29 non-Iberian donor groups (Methods). We extended the fineSTRUCTURE model to re-cluster individuals within Iberia, now based only on their levels of ancestry sharing across these 29 groups (Methods). These clusters capture the impact of migration into and across Spain, removing the effects of simple isolation events.Next, we sought to characterize the relationship between Iberians  and non-Iberian groups, to understand the extent to which recent migrations from outside Iberia have influenced modern-day DNA in Spain. We constructed a combined dataset  of 2919 individuals from Spain, Europe, north AfricaUsing this approach we inferred six distinct clusters within Iberia Fig. , many fe6. Only six of the 29 donor groups show a contribution >1% in Iberia, and all are located in Western and Southern Europe, and north-west Africa  all 29 donor groups to approximate the unknown ancestral groups that actually contributed to modern-day Iberian individuals (Methods). This approach accounts for the stochasticity in ancestral relationships along the genome and was previously shown to be informative in the context of the British Islesica Fig. , Methods25 to each of our six clusters (Methods). GLOBETROTTER infers dates of admixture and the make-up of the source populations, and tests whether admixture patterns are consistent with a simple mixing of two groups at a single time in the past, compared to more complex alternative models. GLOBETROTTER found strong evidence (p < 0.01) of admixture for all six clusters  spans no more than 11 generations (~300 years) and all confidence intervals combined span less than 14 generations (< 400 years).To distinguish between possible scenarios that could produce these patterns, we applied the GLOBETROTTER method30. If this European-like ancestry had arrived more recently than the detected admixture event, the north Moroccan donor group would be a poor proxy for the historical source population and GLOBETROTTER would use a better alternative. Since GLOBETROTTER detects admixture based on the DNA received by the target population (Iberia) this would not affect the date estimates25.GLOBETROTTER shows a subtle preference for Western Sahara as a source of north African DNA, as opposed to north Morocco. This might be explained if modern-day north Moroccan haplotypes are more similar to present-day Spanish individuals than the historical source population was. Indeed, a mixture analysis we performed of the north Moroccan group itself  shows that this group has a non-trivial proportion of European-like ancestry while the Western Sahara donor group has none. Previous work showed similar resultsOur earlier results imply the incorporation of Basque-like DNA elsewhere within Spain. We next incorporated the clade labelled ‘Basque1’ as a potential donor group, to characterise and date this event (Methods). Ancestry profiles show contributions of Basque-like DNA Fig.  highest Reconquista, during which Christian-controlled territory in the north moved gradually southwards from the mid-8th Century, following the Muslim conquest of Iberia (711CE). By 1249, almost all of Iberia was under Christian rule, and the Battle of Granada in 1492 marks the end of Muslim rule in Iberia. There is historical evidence of migration of peoples from the northern Christian kingdoms into newly conquered regions during the Reconquista32. The east–west boundaries of the clusters we see in the north of Spain correspond closely to the regions of broad linguistic differences in the Christian-ruled north, which date back to at least the first 200 years of Muslim rule  including in Galicia, despite the fact that the region of Galicia as it is defined today (north of the Miño river), was never under Muslim rule35. Our results go beyond this, showing that by leveraging information genome-wide, it is possible to detect subtle genetic structure at fine geographic scales, without utilizing prior geographic information. It will be interesting to identify whether (and if so which) other parts of the world show similar patterns. Pairs of individuals within these clusters show high levels of coancestry relative to the rest of Spain 36, and in this study is observed down to scales of <10 km. Such strong, localised genetic drift predicts the existence of geographically localised rare mutations, including pathogenic ones. For example, cases of a specific form of inherited ataxia (SCA36) cluster within a specific region of Galicia37. Our results imply this phenomenon will tend to happen in specific areas, and can arise even where population density is high and in the absence of obvious strong geographic barriers, such as Galicia in the case of Spain.It appears that within-country population structure occurs across the world38. Biological samples were sourced from a variety of hospitals across Spain as well as the Spanish National DNA Bank. All samples were assayed together by Affymetrix (now Thermo Fisher Scientific) in the same facility. See ref. 38 for full details of sample collection and genotype calling. We used both cases and controls in Phase I of the GWAS study, which totalled 1548 individuals prior to quality control. Individuals from all 17 of Spain’s autonomous communities are represented in this dataset, but the Spanish territories of Melilla, Ceuta, and the Canary Islands are excluded from analyses involving geographic labelling due to limited sampling in these areas (four samples).For the Spain-only analysis we used genotype data that was originally collected and typed for a colorectal cancer GWAS39 with a reference panel and recombination map from Phase I 1000 Genomes40. Close relatives (kinship coefficient > 0.1), and individuals with evidence of recent, non-Spanish ancestry were included in the phasing, but excluded from the fineSTRUCTURE analysis. This procedure resulted in 1413 samples and 693,092 SNPs for our main analysis.After applying a series of quality control filters  the dataset comprised 2919 individuals and 300,895 SNPs, which we used in our joint analyses. See Supplementary Note For all analyses involving individuals from outside of Spain we combined four sources of genotype data: European samples from POPRES28, which uses a model-based approach to cluster individuals with similar patterns of shared ancestry. Within the fineSTRUCTURE framework, shared ancestry is measured as the total amount of the genome (in centiMorgans (cM)) for which individual i shares a common ancestor with individual j, more recently than all the other individuals in the sample. This is estimated for each pair of individuals i and j, defining a square matrix referred to as the coancestry matrix  rather than the total amount of genome, but we found the alternative measure to be more robust to artefacts such as genotyping error  procedure to find a high posterior probability partition of individuals into a set of clusters. The number of clusters is not specified in advance, but rather estimated under the fineSTRUCTURE probability model. Having found a set of clusters, fineSTRUCTURE then infers a hierarchical tree by successively merging pairs of clusters whose merging gives the smallest decrease to the posterior probability (of the merged partition) among all possible pairwise merges.(A)Spanish individuals(B)Spanish and Portuguese individuals(C)Non-Spanish individualsWe ran several fineSTRUCTURE analyses using phased haplotypes from the following sets of individuals:28 to estimate the coancestry matrix, followed by fineSTRUCTURE’s clustering and tree-building procudures. We used a previous successful application of fineSTRUCTURE6 as a guide for the number of iterations in the MCMC, and other required parameters 28 to est6, which uses the information from multiple samples of the fineSTRUCTURE MCMC. Informally, the procedure measures the overlap between a cluster k, and individual i’s assigned cluster in each of the MCMC samples within a single fineSTRUCTURE run. This can take values between 0 and 1, and sums to 1 across all clusters for a given individual. It provides a measure of certainty about the assignment of individual i to each cluster k in the final set of clusters. The cluster assignment certainty for an individual is the value corresponding to the final cluster assignment, and will be close to 1 if they are assigned to a cluster with largely the same set of individuals in each MCMC sample. This measure can be obtained for different layers of the hierarchical tree by summing the values for the clusters that merge at each layer.For analysis A, we measured uncertainty in the assignment of individuals to clusters by using a procedure described formally in ref. For fineSTRUCTURE analysis A, cluster assignments at higher levels of the hierarchical tree are typically more certain than lower-level clusters. At the broader level of the tree shown  such that all clusters were larger than size 20. Recall that moving up the tree, at each level a pair of clusters is merged. At the base of the tree, newly separated groups typically contain few (<15) individuals, with higher clustering uncertainty. To avoid the presence of such minor clusters, we traversed up the tree until the first time a merge occurred between two larger clusters . This occurs at the level of 49 clusters. However, at this level over half (28) of the clusters are within the clade involving individuals mostly from south-west Galicia  and coding them to the centre of the matching region. Some locations were not themselves a municipality, so we coded these individuals to the centre of the nearest municipality, identified by using Google Maps. However, in the fineSTRUCTURE analysis we clustered all individuals, i.e. also included those individuals for whom the exact birthplaces of their grandparents was not known, but these are not used in interpreting the spatial distribution of the inferred genetic structure.Geographic information was available for individuals in the Spanish cohort, along with their age at collection, sex, and genotyping plate (controls only) and batch used in genotype calling. The geographic information includes region of origin (autonomous community) for all individuals; for 959 individuals (68%) the birthplace (town) of at least one grandparent was available, and for 883 of these the birthplace of all four grandparents was available. We assigned each individual to a geographic coordinate by matching the text (e.g. ‘Barcelona’) to a municipal region as defined by the Spanish Statistical Office ), which we adapted in the following ways using Adobe Illustrator. Colours were added to help distinguish the different linguistic groups and the political borders  were overlaid onto the right-most map.The linguistic maps in Fig. i shares its most recent common ancestor with another individual j, out of all the individuals in the sample. Properties of this matrix are informative of patterns of drift and admixture within and across clusters inferred by fineSTRUCTURE. Specifically, excess coancestry between individuals in the same cluster (within-cluster coancestry) is a natural measure of genetic drift of that cluster relative to all the other clusters28. In general, individuals are often observed to have the highest levels of coancestry with other individuals in their assigned cluster. This is not a constraint of the fineSTRUCTURE model; rather it is because if two individuals have similar patterns of shared ancestry, they are naturally also likely have more recent shared ancestry between them. However, it is possible for this not to be the case, and this is informative of admixture  measures the amount of genome (in cM) for which an individual p-value using the number of resamples (S) for which the mean within-cluster coancestry is smaller than the mean coancestry with each of the other clusters. That is: We looked for such signals in the case of Spain by testing whether a cluster (inferred by fineSTRUCTURE) has a within-cluster coancestry that is, on average, smaller than its coancestry with another cluster. We used the 26 clusters  by applying the ‘--indep-pairwise r2’ command in PLINK (v1.7)42, and excluded regions of long-range LD derived from43. We computed principal components of the genotype calls using the software Shellfish44, and FST between different groups of individuals using EIGENSOFT (v5.0.1)45. We conducted two FST analyses: one using autonomous community as group labels, and the other using clusters inferred by fineSTRUCTURE as group labels.For both PCA and FST analysis using autonomous community as group labels the strongest differentiation  is between the Basque-speaking regions and all other regions, and between Galicia and other regions   All individuals combined (excluding Spanish but including Portuguese)(CII) Individuals from north Africa only(CIII) Individuals from Europe only28, we obtained fineSTRUCTURE results for the north African and European groups independently. That is, using coancestry matrices that only allow copying within each set of individuals in CII and CIII, respectively. We then defined a set of donor groups based on the clusters and hierarchical trees inferred by fineSTRUCTURE in these three analyses. We considered the following factors in defining donor groups. Ideally, each donor group would contain about the same number of samples, and not be too small. Donor groups should also be relatively homogeneous with respect to their shared ancestry with the population of interest (in this case Iberia), although could be heterogeneous within themselves. We therefore prioritised donor group size over capturing finer scale structure that might exist within donor groups themselves. See Supplementary Note In analysis CI, fineSTRUCTURE cleanly split the three main groups corresponding to Europe, north Africa, and sub-Saharan Africa, as well as inferring finer sub-structure. However, in order to maximise the power to detect finer scale structure6.One cluster in the fineSTRUCTURE analysis CIII overlaps significantly (98%) with the individuals with grandparental origins in Portugal as reported by the data source (POPRES). For the purposes of the analyses in this chapter , this group of 117 individuals is referred to as ‘Portugal’ or ‘Portuguese individuals’  × (N + M) square matrix C, such that,Y contains zeros, except for each of the (block) diagonal entries corresponding to pairs of individuals within the same donor population k. These entries each take the value gk, which is determined such that the mean of all the entries corresponding to donor population k are the same for the sub-matrix X as the sub-matrix Y. The zeros in the matrix C have the effect of not allowing any copying from or to the Iberian individuals to contribute to the fineSTRUCTURE likelihood. We then run fineSTRUCTURE algorithm using the force file option (-F), where each ‘continental’ group is a donor group, thus only allowing splits and merges to take place among Iberian individuals. We used a c-factor of 0.0579, which was computed in the manner described in Supplementary Note Here we describe the method we used to infer clusters of Iberian individuals with distinct patterns of haplotype sharing with external groups. We used the fineSTRUCTURE clustering algorithm but with a modified version of the input coancestry matrix. Specifically, we compute the coancestry (using CHROMOPAINTER) between each Iberian individual and each of the non-Iberian individuals, as described above, but only allowing Iberian individuals to copy from non-Iberian individuals. This results in a rectangular matrix, 6. Briefly, we use CHROMOPAINTER to compute a coancestry vector for each Iberian individual, where we only allow them to copy from haplotypes in the donor groups (as with matrix X above), and then average the coancestry vectors within each cluster and donor group. For each Iberian cluster i, we then find a smaller set of donor groups which together (as a non-negative linear mixture whose coefficients sum to 1) best explains its cluster-averaged coancestry vector, i, and its elements sum to 1. Results for six Iberian clusters are shown in Fig. We estimated ancestry profiles for each of the Iberian clusters using the procedure described previouslyi for each chromosome, and summing the observed chromosome-level coancestry vectors across all chromosomes. We then compute 1000 such re-estimations and report the range of the inner 95% of the resulting bootstrap distribution.We measured uncertainty in these ancestry profiles by re-estimating the cluster-averaged coancestry vectors using a set of pseudo individuals. Each pseudo individual is formed by randomly selecting an individual in cluster s in a fine spatial grid across Spain. We then compute ancestry profiles for each of the grid-points in the same way as for the Iberian clusters (described above), but setting s and individual i located within the borders of Portugal we set all The availability of fine-scale geographic information for many of the Spanish individuals allowed us to estimate the spatial distribution of shared ancestry Fig. . Instead25. Briefly, GLOBETROTTER uses ‘paintings’ from the CHROMOPAINTER algorithm to construct coancestry curves. These curves measure the rate of decay of linkage disequilibrium with genomic distance, between sites with ancestry from a pair of source populations. The parameters of exponential functions fitted to these curves (decay rates and intercepts) are used to estimate admixture dates, admixture proportions, and the best fitting mix of modern-day groups that characterise the ancestral populations involved in an admixture event.We used the GLOBETROTTER algorithm to estimate dates, proportions and configurations of admixture eventsWe conducted two analyses using GLOBETROTTER. The first (gtA) was designed to detect admixture event(s) in the history of Iberia that might involve any combination of non-Iberian source populations, without any prior assumptions on the nature of the event. The second analysis (gtB) was designed to detect only admixture event(s) involving a Basque-like source population, i.e. based on a prior hypothesis. In each case we defined a set of target groups within which to look for an admixture event; a set of donor groups, which we allow to be donors in the initial painting; and a set of surrogate populations, which we allowed GLOBETROTTER to consider as components of any admixture event . GLOBETROTTER automatically tests for these using a series of criteria based on how well the coancestry curves fit the models for different types of admixture scenarios27. All other data used in the paper were previously published and are publicly available for research use.The collection of the Spanish genotype data was approved by the “Comité Ético de Investigación Clínica de Galicia”, and each of the institutional review boards of the participating hospitals. All samples were obtained with written informed consent reviewed by the ethical board of the corresponding hospital, in accordance with the tenets of the Declaration of Helsinki. A previous analysis of these data was published in 2013Some specialist code used in the paper, which is not already publicly available, will be made available for academic research on request. We used the following versions of software: Birdsuite v1.4, KING v1.4, QCTOOL v1, SHAPEIT v2, PLINK v1.7, CHROMOPAINTER v2, fineSTRUCTURE v0.0.5, GLOBETROTTER v3, EIGENSOFT v5.0.1. Details of parameters used in particular analyses are in the relevant section of Methods or Supplementary Information.Supplementary InformationPeer Review File