First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function. Unique among body organs, the human brain is a vast network of information processing units, comprising billions of neurons interconnected through trillions of synapses. Diverse neuronal and non-neuronal cells display a wide range of molecular, anatomical, and physiological properties that together shape the network dynamics and computations underlying mental activities and behaviour. Brain networks self-assemble during development, leveraging genomic information shaped by evolution to build a set of stereotyped network scaffolds that are largely identical among individuals; life experiences then customize neural circuits in each individual. An essential step towards understanding the architecture, development, function and diseases of the brain is to discover and map its constituent elements of neurons and other cell types.5. Neurons are remarkably complex and heterogeneous, both locally and in their long-range axonal projections, which can span the entire brain and connect to many target regions. Many conventional techniques analyse one neuron at a time, and often study only one or two cellular phenotypes in an incomplete way . As a result, despite major advances in past decades, phenotypic analyses of neuron types have remained severely limited in resolution, robustness, comprehensiveness and throughput. Complexities in the relationship between different cellular phenotypes  have fuelled long-standing debates on neuronal classification6.The notion of a ‘neuron type’, with similar properties among its members, as the basic unit of brain circuits has been an important concept for over a century; however, rigorous and quantitative definitions have remained surprisingly elusive11. Similarly, single-cell DNA methylation and chromatin accessibility studies have begun to reveal cell-type-specific genome-wide epigenetic landscapes and gene regulatory networks in the brain15. Notably, the scalability and high information content of these methods enable comprehensive quantitative analysis and classification of all cell types, which are readily applicable to brain tissues across species and provide a quantitative means of comparative analysis17.Single-cell genomics technologies provide unprecedented resolution and throughput to measure the transcriptomic and epigenomic profiles of individual cells and have rapidly influenced many areas of biology including neuroscience, promising to catalyse a transformation from phenotypic description and classification to a mechanistic and explanatory molecular genetic framework for the cellular basis of brain organization. The application of single-cell RNA sequencing (scRNA-seq) to the neocortex and other brain regions has revealed a complex but tractable hierarchical organization of transcriptomic cell types that are consistent overall with knowledge from decades of anatomical, physiological and developmental studies but with an unmatched level of granularity19. Imaging-based single-cell transcriptomics and its combination with functional imaging, and integration of electrophysiology and single-cell sequencing, enable mapping of the spatial organization and key phenotypic properties of molecularly defined cell types24. Finally, molecular classification of cell types enables genetic access to specific cell types using transgenic mice27 and, more recently, enhancer-based viral vectors32. All of these methods have been applied to brain tissues in independent studies, but not yet in a coordinated fashion to establish how different modalities correspond with one another, and whether a molecular genetic framework is explanatory for other functionally important cellular phenotypes.Other recent technological advances provide the resolution and throughput to analyse whole-brain neuronal morphology and comprehensive projection mapping33. A key concept is the Brain Cell Census, similar conceptually to a population census, that defines the constituent neuronal and non-neuronal cell types and their proportions, spatial distributions and defining phenotypic characteristics. This cell-type classification, organized as a taxonomy, should aim for consensus across modalities and across mammalian species for conserved types. Beyond the cell census, a Brain Cell Atlas would be embedded in a 3D common coordinate framework (CCF) of the brain34, in which the precise location and distribution of all cell types and their multi-modal features are registered and displayed. This spatial framework facilitates integration, interpretation and navigation of various types of information for understanding brain network organization and function.The overarching goal of the BRAIN Initiative Cell Census Network (BICCN) is to leverage these technologies to generate an open-access reference brain cell atlas that integrates molecular, spatial, morphological, connectional and functional data for describing cell types in mouse, human and non-human primate36. We describe a synthesis of eleven companion studies through a coordinated multi-laboratory effort. In these studies, we derive a cross-species consensus molecular taxonomy of cell types using scRNA-seq or single-nucleus RNA sequencing (snRNA-seq), DNA methylation and chromatin accessibility data40. In mouse, we map the spatial cellular organization by multiplexed error-robust fluorescence in situ hybridization (MERFISH)41, characterize morphological and electrophysiological properties by multimodal profiling using patch clamp recording, biocytin staining and scRNA-seq (Patch-seq)43, describe the cellular input–output wiring diagrams by anterograde and retrograde tracing44, identify glutamatergic neuron axon projection patterns by Epi-retro-seq45, Retro-MERFISH41 and single-neuron complete morphology reconstruction46, and describe transgenic driver lines targeting glutamatergic cell types on the basis of marker genes and lineages47. Finally, we integrate this information into a cohesive description of cell types in MOp. These datasets are organized by the BRAIN Cell Data Center (BCDC) and made public through the BICCN web portal (https://www.biccn.org). Key concepts and terms are described in Extended Data Table Here we present the cell census and atlas of cell types in the primary motor cortex  of mouse, marmoset and human , suggesting another level of regulation in defining single-cell connectional specificity.Cell-type transcriptional and epigenetic signatures guide the generation of genetic tools for targeting glutamatergic pyramidal neuron types and fate mapping their progenitor types.Multi-site coordination within BICCN and data archives enabled a high degree of standardization, computational integration and creation of open data resources for community dissemination of data, tools and knowledge.Major findings:37. The combined sc/snRNA-seq datasets contained a large number of cells profiled using both droplet-based and deep full-length sequencing methods  and LIGER, to combine the transcriptomic and epigenomic datasets and derive an integrated molecular taxonomy consisting of 56 neuronal cell types 37  and transcriptional regulatory networks. Similarly, we established M1 cell-type taxonomies for human (127 t-types) and marmoset (94 t-types) by unsupervised clustering of snRNA-seq data, followed by integration with epigenomic datasets38.A mouse MOp molecular taxonomy was derived from seven scRNA-seq and snRNA-seq (sc/snRNA-seq) datasets and single-nucleus methylcytosine sequencing (snmC-seq2) and single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) datasets)37 Fig. . This inLamp5, Sncg and Vip) and three medial ganglionic eminence (MGE)-derived subclasses ), layer and projection pattern in mouse for glutamatergic excitatory neurons , extratelencephalic (ET), corticothalamic (CT), near-projecting (NP) and layer 6b (L6b)), and non-neuronal functional subclasses  .To establish a consensus classification of MOp and M1 cell types among mouse, human and marmoset, we integrated snRNA-seq datasets across species and identified 45 conserved t-types, including 24 GABAergic (γ-aminobutyric acid-producing), 13 glutamatergic and 8 non-neuronal types , whereas others aligned several-to-several . This may reflect over- or under-clustering, limitations in aligning highly similar cell types, or species-specific expansion of cell-type diversity.The resolution of this cross-species consensus taxonomy was lower than that derived from each species alone, owing to variation in gene expression across species. The degree of species alignments varied across consensus types Fig. ; some tyn = 40 nuclei) were under-sampled compared with marmoset (n = 463) and mouse , and average expression was not adequately estimated38.We expected that cell types from more recent common ancestors would share more similar gene expression profiles. Indeed, transcriptomic profiles of consensus cell types were more correlated between human and marmoset, and had 25–50% fewer differentially expressed genes than between primate and mouse Fig. . The oneGlutamatergic subclasses expressed 50–450 marker genes and, unexpectedly, the majority of markers were species-enriched Fig. . This ev51, to identify cell types in situ and map their spatial organization. We selected a panel of 258 genes  on the basis of prior knowledge of marker genes for major cortical cell types and genes identified using sc/snRNA-seq data, and we imaged approximately 300,000 individual cells across MOp and adjacent areas41.We used MERFISH, a single-cell transcriptome imaging methodLamp5, Sncg, Vip, Sst and Pvalb subclasses) and good correspondence at cluster level41.Clustering analysis of the MERFISH-derived single-cell expression profiles resulted in a total of 95 cell clusters in MOp  Fig. , which s41. Notably, IT cells, the largest branch of neurons in the MOp, formed a largely continuous gradient of cells with correlated gradual changes between their expression profiles and their cortical depths41  identified projection targets of different neuron types in the MOp41 Fig. . Retrogr 41 Fig. . Overall42. We mapped these cells to the mouse MOp transcriptomic taxonomy37  and obtained their transcriptomes using Smart-seq2 sequencingy37 Fig. . Cells wy37 Fig. , therebyy37 Fig. .Fig. 3CoSst interneurons were often characterized by large membrane time constants, pronounced hyperpolarization sag, and rebound firing after stimulation offset. However, within each subclass, there was substantial variation in morpho-electric properties between t-types. This variation was not random but organized such that transcriptomically similar t-types had more similar morpho-electric properties than distant t-types. For example, excitatory t-types from the IT subclasses with more similar transcriptomes were also located at adjacent cortical depths, suggesting that distance in t-space co-varied with anatomical distance42, even within a layer , and cortico-subcortical projecting neurons in L5 ET. Many cortico-thalamic projecting neurons were also observed in L6 CT  levels to integrate the L5 ET Epi-retro-seq cells with the ALM Retro-seq cells and observed enrichment of MY-projecting cells in the same cluster45.Enrichment of L5 ET neurons with Epi-retro-seq (40.2% versus 5.62% in unbiased profiling of MOp using snmC-seq2) enabled investigation of subtypes of L5 ET neurons known to project to multiple subcortical targets in TH, VTA+SN, pons and MYers Fig. . MY-projers Fig. , in agre12. We identified 511 differentially CH-methylated genes (CH-DMGs) and 58,680 CG-DMRs across the L5 ET clusters  are reliable markers of regulatory elements such as enhancersers Fig. . We alsoers Fig. . For exa25. Here we present a genetic toolkit for dissecting and fate-mapping glutamatergic pyramidal neuron (PyN) subpopulations largely on the basis of their developmental genetic programs.Genetic access to specific neural subpopulations and progenitors is necessary for multi-modal analyses to validate t-types, fate-map their developmental trajectories, and study their function in circuit operation55 12.5 RGs in the dorsal neuroepithelium, distributed along a medial-high and lateral-low gradient, consistent with their mRNA expression at this stage60. These RGs generated PyNs across all cortical layers, suggesting multipotency  generate PyNs either directly or indirectly through intermediate progenitors (IPs)55 Fig. . Temporanes Fig.  that faincy Fig. .Fig. 5GePlxnd1 (ITPlxnd1), L5b and L6 for ET-Fezf2 (ETFezf2), L6 for CT-Tle4 (CTTle4). Anterograde projection tracing in MOp of adult animals demonstrated that ITPlxnd1 projected to multiple ipsilateral and contralateral cortical areas and to STR/caudate putamen (CP); ETFezf2 projected robustly to several ipsilateral cortical sites, CP and numerous subcortical targets including TH, MY and corticospinal tract; CTTle4 projected specifically to a set of thalamic nuclei47  neurons resided in L5a and projected ipsilaterally to multiple cortical areas, contralaterally to homotypic and heterotypic areas, and bilaterally to CP  neurons resided in L2; although they also projected to ipsilateral cortical and striatal targets, and to homotypic contralateral cortex, they extended minimal projections to heterotypic contralateral cortex and CP ) and retrograde (cholera toxin b (CTb)) tract tracing44  region using classic anterograde , ET  and CT (Ntsr1 and Tle4) subclasses with distinct laminar distributions62.We generated a fine-grained areal and laminar distribution map of multiple MOp-ul projection neuron populations using retrograde tracing44 , ventral anterior-lateral complex (VAL) and ventral medial nucleus (VM)) and cortical areas such as MOs and SSp.Viral tracers were used to systematically examine MOp-ul cell subclass-specific inputs and outputs46, augmented with publicly available single-cell reconstructions from the Janelia Mouselight project18. Additional analysis was also conducted using BARseq64. This analysis revealed a rich diversity of projection patterns within the IT, ET and CT subclasses , Rspo1 for L4/5 IT (1), Htr2c for L4/5 IT (2-3), and Rorb for L4/5 IT and L5 IT  and uniform manifold approximation and projection (UMAP) embedding of all IT neurons (excluding the highly distinct L6 IT Car3 cells) from 11 mouse molecular datasets, including 6 sc/snRNA-seq datasets, and the snmC-seq2, snATAC-seq, Epi-retro-seq, MERFISH and Patch-seq data Fig. . This repo1 Fig. , a L4 cevel Fig. , probabl9 after co-clustering all the SMART-seq glutamatergic transcriptomes from both regions ons Fig. . In UMAP42, cells from the L4/5 IT_1 type had no or minimal apical dendrites without tufts in L1, in contrast to cells from the L2/3 IT, L4/5 IT_2 and L5 IT types, which had tufted apical dendrites  exhibited two local morphological features typical of L4 neurons from sensory cortices  type and Npnt for SCF L5 ET (2-3) types  display a largely gradual transition across cortical depth or layers   and master transcription factors specific to neuronal subclasses in the combined transcriptomic and epigenomic datasets Fig. . We founUnderstanding the principles of brain circuit organization requires a detailed understanding of its basic components. The current effort combines a wide array of single-cell-based techniques to derive a robust and comprehensive molecular cell-type classification and census of the primary motor cortex of mouse, marmoset and human, coupled with a spatial atlas of cell types and an anatomical input–output wiring diagram in mouse. We demonstrate the robustness and validity of this classification through strong correlations across cellular phenotypes, and strong conservation across species. Together these data comprise a cell atlas of the primary motor cortex that encompasses a comprehensive reference catalogue of cell types, their proportions, spatial distributions, anatomical and physiological characteristics, and molecular genetic profiles, registered into a CCF. This cell atlas establishes a foundation for an integrative study of the architecture and function of cortical circuits akin to reference genomes for studying gene function and genome regulatory architecture. Furthermore, it provides a map of the genes that contribute to cellular phenotypes and their epigenetic regulation. These data resources and associated tools enabling genetic access for manipulative experimentation are publicly available. This body of work provides a roadmap for exploring cellular diversity and organization across brain regions, organ systems and species.10, our multimodal cross-species study of the primary motor cortex suggests that a general principle of cortical cell-type organization is its hierarchical relationship, whereby high-level classes linked by major branches comprise progressively finer subpopulations connected by minor branches. In this scheme, the higher-level classes and subclasses are categorically and concordantly distinct from each other across modalities, are conserved across species, and probably arise from different developmental programs, such as GABAergic neuron derivatives of different zones of the ganglionic eminences or the layer-selective glutamatergic neurons derived sequentially from progenitors of the cortical plate. At the lower branch levels (types or clusters), however, while certain cell types are highly distinct , distinctions and boundaries among many other clusters can be ambiguous and vary among different modalities.Substantiating previous studies69, is the coexistence of discrete and continuous variations of cell features across modalities at the lower branch level. A compelling example is the continuous and concordant variation of transcriptomic, anatomical and physiological properties along cortical depth within multiple cell populations, including the glutamatergic L2/3–L6 IT and GABAergic Sst and Pvalb subclasses. Although some of the variations may result from technical factors, such as differences in the resolution of measurements across data modalities (with transcriptomics providing the highest granularity at present), a major source of these continuous variations may reflect true biology, supported by the coordinated variation across transcriptomic, spatial, morphological and physiological properties as shown by MERFISH and Patch-seq. Therefore, another emerging principle of cell type organization is the coexistence of discrete and continuous variations that underlie cell-type diversity.In this context, another important finding, consistent with and building on multiple other studies11. Region-specific connectivity patterns of similar molecular types may be a major factor defining the functional specificity of the primary motor cortex.Together, the principles of hierarchical organization comprising discrete classes and types as well as continuum within and across subpopulations represent a more nuanced and biologically realistic description of cell-type landscape, with implications in cell classification and census. For example, the multimodal variations at finer granularity may preclude a fully discretized representation of cell types with consistency across cell phenotypes, and may explain some of the discrepancies in estimated numbers of cell types using different approaches. An intriguing question is whether continuous variations of cell features will increase further or become more discretized in the context of neural circuit operation, converging to a set of distinct functional elements from a more continuous cellular landscape. An example of this is regionalization. We identify a MOp-specific input–output wiring diagram—however, transcriptomic cell types are generally shared between MOp and its neighbouring cortical areasOur findings have major implications for understanding the biological basis of cellular identity towards a more rigorous, quantitative and satisfying definition and classification of cell types. First and foremost, our discovery of the compelling correspondence across molecular genetic, anatomical and physiological features of hierarchically organized cell populations, reflecting developmental origins and mainly conserved across mammalian species, demonstrates the biological validity and genomic underpinning of major cell types. These findings establish a unifying and mechanistic framework of cell-type classification that integrates multi-layered molecular genetic information with multi-faceted phenotypic properties. Thus, single-cell transcriptomics and epigenomics can serve as powerful approaches for establishing a foundational framework of cell types, owing to not only their unparalleled scalability but also to their representation of the underlying molecular genetic programs rooted in development and evolution. Physiological, morphological and connectional characterizations assign functional attributes to cells; their concordance with molecular identities provides strong validation to the molecularly defined cell types, whereas their differential variations reveal additional, probably network- and activity-driven factors that contribute to further refinement of cell types.37. Looking ahead, it is important to note that at more refined levels, the number of cell types that can be distinguished will probably change with additional cellular features characterized at greater breadth and depth using new methods and approaches.While the higher levels of the hierarchy comprise ~around 25 subclasses  that are identified with remarkable consistency across multiple species and experimental modalities, many finer levels of cell properties do not neatly segregate into discrete and consistent sets of cell types with perfect correspondence among data modalities. These include aspects of continuous distributions, species specializations and mismatches between molecular and anatomical phenotypes that may result from developmental events no longer represented in the adult. Different methods provide somewhat different granularity of clustering, and thus different numbers of putative cell types. For example, single-cell transcriptomics identifies around 100 clusters representing the terminal leaves of this hierarchically branched organizationOverall, the landscape of cell types appears to be generated from a combination of specification through evolutionarily driven and developmentally regulated genetic mechanisms, and refinement of cellular identities through intercellular interactions within the network in which the cells are embedded. In this scenario, genetic mechanisms drive intrinsic or cell-autonomous determination of cell fate, as well as progressive temporal generation of cell types from common progenitor pools that explain global similarities and continuous features of cellular phenotypes reflecting developmental gradients. Network influences can drive further phenotypic refinement that may not be reflected in the adult genetic signature—for example for axonal projection and synaptic connectivity that may reflect transient or stochastic developmental events, region or circuit-specific and/or activity or plasticity-dependent modification to form and reshape functionally specific circuits. Future studies focusing on these mechanisms and testing of the ensuing hypotheses will enable a deeper understanding of the nature of variability among related cell types in the mammalian brain.70.Evolutionary conservation is strong evidence of functional significance. The demonstrated conservation of cell types from mouse, marmoset, macaque and human suggests that these conserved types have important roles in cortical circuitry and function in mammals and even more distantly related species. We also find that similarity of cell types varies as a function of evolutionary distance, with substantial species differences that represent either adaptive specialization or genetic drift. For the most part, species specializations tend to appear at the finer branches of the hierarchical taxonomy. This result is consistent with a recent hypothesis in which cell types are defined by common evolutionary descent and evolve independently, such that new cell types are generally derived from existing genetic programs and appear as specializations at the finer levels of the taxonomic treeA surprising finding across all homologous cell types was the relatively high degree of divergence for genes with cell-type-specific expression in a given species. This observation provides a clear path to identify core conserved genes underlying the canonical identity and features of those cell types. Furthermore, it highlights the need to understand species adaptations superimposed on the conserved program, as many specific cellular phenotypes may vary across species including gene expression, epigenetic regulation, morphology, connectivity and physiological properties. As we illustrated in the Betz cells, there is clear homology across species in the L5 ET subclass, but variation in many measurable properties across species.Our findings have major implications for the consideration of model organisms to understand human brain function and disease. Despite major investments, animal models of neuropsychiatric disorders have often been characterized by ‘loss of translation’, fuelling heated debates about the utility of model organisms in the development of treatments for human diseases. Cell census information aligned across species will be highly valuable for making rational choices about the best models for each disease and therapeutic target. For example, the characterization of cell types and their properties shown in Fig. The approach we took to generate a cell census and atlas through a systematic dissection of cell types opens up numerous avenues for future work. The MOp census and atlas provides a foundational platform for the broad neuroscience community to accumulate and integrate cell-type information across species. Classification of cell types based on their molecular, spatial and connectional properties in the adult sets the stage for developmental studies to understand the molecular genetic programs underlying cell-type specification, maturation and circuit assembly. The molecular genetic information promises to deliver tools for genetic access to many brain cell types via transgenic and enhancer virus strategies. A combination of single-cell transcriptomics and functional measurements may further elucidate the roles of distinct cell types in circuit computation during behaviour, bridging the gap between molecular and functional definition of cell types. The systematic, multi-modal strategy described here can be extended to the whole brain, and major efforts are underway in the BICCN to generate a brain-wide cell census and atlas in the mouse with increasing coverage of human and non-human primates.Car3), the layer 5 neurons projecting to extratelencephalic targets (L5 ET), the CT-projecting neurons in layer 6 (L6 CT), the NP neurons found in layers 5 and 6, and the L6b neurons whose projection patterns remain largely unknown.In this manuscript we have adopted a nomenclature for major subclasses of cortical glutamatergic excitatory neurons, which have long-range projections both within and outside of the cortex, following a long tradition of naming conventions that often classify neurons based on their projection targets. This nomenclature is based on our de novo transcriptomic taxonomy Fig.  that orgWhile the IT, CT, NP and L6b neurons have been consistently labelled as such in the field, the L5 ET neurons have not been named consistently in the literature, largely owing to their large variety of projection targets and other phenotypic features that vary depending on cortical areas and species. Here we use the term L5 ET  to refer to this prominent and distinct subclass of neurons as a standard name that can be accurately used across cortical regions and across species, and we provide our rationale below.48. Accordingly, various names incorporating these features have been adopted to refer to L5 ET versus L5 IT cells, such as L5b versus L5a, thick-tufted versus thin-tufted and burst-firing versus regular-firing. The most common term used to refer to L5 ET cells residing in motor cortical areas has been PT, which refers to neurons projecting to the pyramidal tract. As accurately stated in Wikipedia, “The pyramidal tracts include both the corticobulbar tract and the corticospinal tract. These are aggregations of efferent nerve fibers from the upper motor neurons that travel from the cerebral cortex and terminate either in the brainstem (corticobulbar) or spinal cord  and are involved in the control of motor functions of the body.”It has long been appreciated that cortical layer 5 contains two distinct populations of neurons that can be distinguished, not only based on the presence or absence of projections to ET targets (ET and IT cells), but also based on their predominant soma locations, dendritic morphologies and intrinsic physiology73 and prominently highlighted in this manuscript, it is now clear that PT represents only a subset of L5 ET cells and is thus unable to accurately encompass the entire L5 ET subclass. This realization is informed by comparisons across species and cortical areas, and by single-cell transcriptomics and descriptions of the projections of single neurons, as well as studies linking transcriptional clusters to projection targets.Owing to the past wide use of the term PT, we do not take the decision to use L5 ET rather than PT lightly. However, in the face of multiple lines of evidence that have accumulated over the last several years38  PT refers to motor neurons that project into MY or spinal cord, but in many cortical areas  none of the L5 ET cells are motor neurons; and 2) even in the motor cortex many cells in the L5 ET subclass do not project to the pyramidal tract and instead project solely to the TH (or to TH and other non-PT targets). This is revealed by single-neuron reconstructions53 Figs. , 8, BARs even in 49. Given that the telencephalon is equivalent to the cerebrum, ET and subcerebral have the same meaning and the term L5-SCPN would be an accurate and equivalent alternative. But the ‘L5’ qualifier is crucial in either case to distinguish these cells from the L6 CT subclass. We favour the use of ET because SCPN has not been widely adopted and due to symmetry with the widely used ‘IT’ nomenclature. Alternatively, given their evidence that “unlike pyramidal tract neurons in the motor cortex, these neurons in the auditory cortex do not project to the spinal cord”, Chen et al64 used the term ‘pyramidal tract-like’ (PT- l). We also favour L5 ET over L5 PT-l which clings to an inaccurate and now outdated nomenclature.We recognize that another name that has been used to describe L5 ET cells is subcerebral projection neuron (SCPN)38. Code for generating Figs. http://data.nemoarchive.org/publication_release/Lein_2020_M1_study_analysis/Transcriptomics/flagship/. Analysis was performed in RStudio using R version 3.5.3, R packages: Seurat 3.1.1, ggplot2 3.2.1 and scrattch.hicat 0.0.22.To identify homologous cell types across species, human, marmoset and mouse 10x v3 snRNA-seq datasets were integrated using Seurat’s SCTransform workflow. Each major cell class  was integrated separately across species. Expression matrices were reduced to 14,870 one-to-one orthologues across the three species . Nuclei were downsampled to have approximately equivalent numbers at the subclass level across species. Marker genes were identified for each species cluster using Seurat’s FindAllMarkers function with test.use set to ‘roc’, > 0.7 classification power. Markers were used as input to guide alignment and anchor-finding during integration steps. For full methods see ref. https://github.com/AllenInstitute/BICCN_M1_Evo). The integrated space (from the previously mentioned Seurat integration) was over-clustering into small sets of highly similar nuclei for each class (about 500 clusters per class). Clusters were aggregated into metacells, then hierarchical clustering was performed based on the metacell gene expression matrix using Ward’s method. Hierarchical trees were then assessed for cluster size, species mixing and branch stability by subsampling the dataset 100 times with 95% of nuclei. Finally, we recursively searched every node of the tree, and if certain heuristic criteria were not sufficient for a node below the upper node, all nodes below the upper node were pruned and nuclei belonging to this subtree were merged into one homologous group. We identified 24 GABAergic, 13 glutamatergic and 8 non-neuronal cross-species consensus clusters that were highly mixed across species and robust. For full methods see ref. 38. A final dendrogram of consensus cell types was constructed by transforming the raw unique molecular identifier (UMI) counts to log2(counts per million (CPM)) normalized counts. Up to 50 marker genes per cross-species cluster were identified by using the scrattch.hicat (v0.0.22) (https://github.com/AllenInstitute/scrattch.hicat) display_cl and select_markers functions with the following parameters; q1.th = 0.4, q.diff.th = 0.5, de.score.th = 80. Median cross-species cluster log2 CPM expression of these genes were then used as input for scrattch.hicat’s build_dend function. This analysis was bootstrapped 10,000 times with branch colour denoting confidence. Branch robustness was assessed by rebuilding the dendrogram 10,000 times with a random 80% subset of variable genes across clusters and calculating the proportion of iterations that clusters were present on the same branch. Consensus taxonomy agreement in Fig. To establish a robust cross-species cell type taxonomy, we applied a tree-based clustering method on integrated class-level datasets . For a given cross-species cluster, each sample was split by species and donor, then a Wald test was performed between each species pair. Genes with adjusted P-values < 0.05 and log2 fold-changes greater than 2 in either direction were counted and reported in Fig. Expression matrices were subsetted to include one-to-one orthologous genes across all three species. Spearman correlations shown in Fig. 75 into a target region in INTACT mice76, which turned on Cre-dependent GFP expression in the nuclei of MOp neurons projecting to the injected target region. Individual GFP-labelled nuclei of MOp projection neurons were then isolated using fluorescence-activated nucleus sorting (FANS)  of each single nucleus.We injected retrograde tracer rAAV2-retro-Cre45. Specifically, we quantified the ratio between the number of cells in expected on-target subclasses  versus in expected off-target subclasses , denoted as rp, and compared the ratio with the one expected from the unbiased data without enrichment for specific projections, denoted as ru. This provides an estimation of signal-to-noise ratio of each FANS experiment. For IT projections, we used IT subclasses as on-target and L6 CT + inhibitory as off-target, and for ET projections, we used L5 ET as on-target and IT + inhibitory as off-target. For the MOp neurons without enrichment of projections, the expected ratio between cells in IT subclasses and in L6 CT + inhibitory are ru = 2,652:1,775, whereas the expected ratio between cells in L5 ET subclass and in IT + inhibitory are ru = 202:3,434. The fold enrichment in the text was computed by rp/ru for each FANS run separately and averaged across IT or ET targets respectively.The methods used to evaluate contamination level and potential reasons are described in detail in ref. We want to point out that, in addition to this computational method, other methods are available to evaluate and minimize potential contamination in Epi-retro-seq. In cases in which differences in expected results from on- versus off-target populations are unknown, other available methods would need to be used to eliminate cases in which injections might have directly labelled cells outside the intended target region, such as examination of labelling along the injection electrode track.37. The read counts were normalized by the total read counts per cell and log transformed. Top 5,000 highly variable genes were identified with Scanpy78 (v1.8.1) and z-score was scaled across all the cells. For Epi-retro-seq, the posterior methylation levels of 12,261 genes in the 848 L5 ET cells were computed45. Top 5,000 highly variable genes were identified with AllCools79 and z-score was scaled across all the cells. The 1,512 genes as the intersection between the two highly variable gene lists were used in Scanorama80 (v1.7.1) to integrate the z-scored expression matrix and minus z-scored methylation matrix with sigma equal to 100.For snRNA-seq, the 4,515 cells from 10x v3 B dataset labelled as L5 ET by SCF were selected45, or from the integrated clustering of multiple datasets37. The integrated clustering and embedding of the 11 datasets are then generated by projecting all datasets into the 10x v2 scRNA-seq dataset using SingleCellFusion79. Genome browser views of IT and ET cell types . MERFISH data were analysed using custom Python code, which is available at https://github.com/ZhuangLab/MERlin.To integrate IT cell types from different mouse datasets, we first take all cells that are labelled as IT, except for L6_IT_Car3, from the 11 datasets as listed in Fig. es Figs.  are take81 v2.2.7.1. using Python 3.6 with parameter: “--nomodel --shift −100 --ext 200 --qval 1e-2 –B --SPMR”. First, we extended peak summits by 250 bp on either side to a final width of 501 bp. Then, to account for differences in performance of MACS2 based on read depth and/or number of nuclei in individual clusters, we converted MACS2 peak scores ) to ‘score per million’82. Next, a union peak set was obtained by applying an iterative overlap peak-merging procedure, which avoids daisy-chaining and still allows for use of fixed-width peaks. Finally, we filtered peaks by choosing a score per million cut-off of 5 as cCREs for downstream analysis.For peak calling in the snATAC-seq data, we extracted all the fragments for each cluster, and then performed peak calling on each aggregate profile using MACS283 with the following parameters: aggregation k = 50, window size = 500 kb, distance constraint = 250 kb. In order to find an optimal co-accessibility threshold, we generated a random shuffled cCRE-by-cell matrix as background and calculated co-accessible scores from this shuffled matrix. We fitted the distribution of co-accessibility scores from random shuffled background into a normal distribution model by using the R package fitdistrplus84. Next, we tested every co-accessible cCRE pair and set the cut-off at co-accessibility score with an empirically defined significance threshold of FDR < 0.01. The cCREs outside of ±1 kb of transcriptional start sites in GENCODE mm10 (v16) were considered distal. Next, we assigned co-accessibility pairs to three groups: proximal-to-proximal, distal-to-distal and distal-to-proximal. In this study, we focus only on distal-to-proximal pairs. We calculated the Pearson’s correlation coefficient (PCC) between gene expression (scRNA SMART-seq) and cCRE accessibility across the joint clusters to examine the relationships between the distal cCREs and target genes as predicted by the co-accessibility pairs. To do so, we first aggregated all nuclei or cells from scRNA-seq and snATAC-seq for every joint cluster to calculate accessibility scores (log2 CPM) and relative expression levels (log2 transcripts per million). Then, PCC was calculated for every gene-cCRE pair within a 1-Mbp window centred on the transcriptional start sites for every gene. We also generated a set of background pairs by randomly selecting regions from different chromosomes and shuffling the cluster labels. Finally, we fit a normal distribution model on background and defined a cut-off at PCC score with an empirically defined significance threshold of FDR < 0.01, in order to select significant positively correlated cCRE-gene pairs.First, co-accessible cCREs are identified for all open regions in all neuron types (cell clusters with less than 100 nuclei from snATAC-seq are excluded) using Cicerocis-regulatory modules based on their relative accessibility across cell clusters. We adapted NMF (Python package: sklearn v.0.24.2) to decompose the cluster-by-cCRE matrix V  into a coefficient matrix H  and a basis matrix W (N × R), with a given rank R: V ≈ WH.We used nonnegative matrix factorization (NMF) to group cCREs into R . Several criteria have been proposed to decide whether a given rank R decomposes the occupancy profile matrix into meaningful clusters. Here we applied a measurement called sparseness85 to evaluate the clustering result. Median values were calculated from 100 times for NMF runs at each given rank with a random seed, which will ensure the measurements are stable. Next, we used the coefficient matrix to associate modules with distinct cell clusters. In the coefficient matrix, each row represents a module and each column represents a cell cluster. The values in the matrix indicate the weights of clusters in their corresponding module. The coefficient matrix was then scaled by column (cluster) from 0 to 1. Subsequently, we used a coefficient > 0.1 (~95th percentile of the whole matrix) as a threshold to associate a cluster with a module. Similarly, we associated each module with accessible elements using the basis matrix. For each element and each module, we derived a basis coefficient score, which represents the accessible signal contributed by all clusters in the defined module.The basis matrix defines module related accessible cCREs, and the coefficient matrix defines the cell cluster components and their weights in each module. The key issue to decompose the occupancy profile matrix was to find a reasonable value for the rank P-value < 0.05, cluster average fold change > 2). To perform the motif enrichment analysis, we used known motifs from the JASPAR 2020 database86 and the subclass specific hypo-CG-DMR identified in Yao et al.37. The AME software from the MEME suite (v5.1.1)87 was used to identify significant motif enrichment  using default parameters and the same background region set as described37. All genes in Extended Data Fig. All analyses for this section were at the subclass level. For RNA expression, we used the scSMART-seq dataset and compared each subclass with the rest of the population through a one-tailed Wilcoxon test and FDR correction to select significantly differentially expressed transcription factors  of Cold Spring Harbor Laboratory, University of California Berkeley and Allen Institute, in accordance with NIH guidelines. Mouse knockin driver lines are being deposited to the Jackson Laboratory for wide distribution.Tle4-2A-CreER, Fezf2-2A-CreER, PlexinD1-2A-CreER, PlexinD1-2A-Flp and Tbr2-2A-CreER were generated by inserting a 2A-CreER or 2A-Flp cassette in-frame before the STOP codon of the targeted gene. Targeting vectors were generated using a PCR-based cloning approach47. In brief, for each gene of interest, two partially overlapping BAC clones from the RPCI-23&24 library (made from C57BL/b mice) were chosen from the Mouse Genome Browser. 5′ and 3′ homology arms were PCR amplified  using the BAC DNA as template and cloned into a building vector to flank the 2A-CreERT2 or 2A-Flp expressing cassette as described27. These targeting vectors were purified, tested for integrity by enzyme restriction and PCR sequencing. Linearized targeting vectors were electroporated into a 129SVj/B6 hybrid ES cell line (v.6.5). ES cell clones were first screened by PCR and then confirmed by Southern blotting using appropriate probes. DIG-labelled Southern probes were generated by PCR, subcloned and tested on wild-type genomic DNA to verify that they give clear and expected results. Positive v6.5 ES cell clones were used for tetraploid complementation to obtain male heterozygous mice following standard procedures. The F0 males and subsequent generations were bred with reporter lines  and induced with tamoxifen at the appropriate ages to characterize the resulting genetically targeted recombination patterns. Drivers Tle4-2A-CreER, Fezf2-2A-CreER and PlexinD1-2A-CreER were additionally crossed with reporter Rosa26-CAG-LSL-Flp and Tbr2-2A-CreER;PlexinD1-2A-Flp with reporter dual-tTA, and induced with tamoxifen at the appropriate age to perform anterograde viral tracing, with Flp- or tTA-dependent AAV vector expressing EGFP (AAV8-CAG-fDIO-TVA-EGFP or AAV-TRE-3g-TVA-EGFP), to characterize the resulting axon projection patterns.Driver and reporter mouse lines were generated using a PCR-based cloning. Knockin mouse lines Npnt and Slco2a1, respectively, using ribonucleoprotein (RNP) complexes composed of SpCas9-NLS protein and in vitro transcribed sgRNA (Npnt: GATGATGTGAGCTTGAAAAG and Slco2a1: CAGTCTGCAGGAGAATGCCT). These RNP complexes were nucleofected into 106 v6.5 mouse embryonic stem cells  along with repair constructs in which P2A-FlpO or P2A-Cre was flanked with the following sequences homologous to the target site, thereby enabling homology-directed repair.To generate lines bearing in-frame genomic insertions of P2A-FlpO or P2A-Cre, we engineered double-strand breaks at the stop codons of Npnt-P2A-FlpO: TGGCCCTTGAGCTCTAGTGTTCCCACTTGCCATAGAAATCTGATCTTCGGTTTGGGGGAAGGGTTGCCTTACCATGCTCCATGAGTGAGCACTGGGAAAAGGGGCAGAGGAGGCCTGACCAGTGTATACGTTCTCTCCCTAGGTCATCTTCAAAGGTGAAAAAAGGCGTGGTCACACGGGGGAGATTGGATTGGATGATGTGAGCTTGAAGCGCGGAAGATGTGGAAGCGGAGCTACTAACTTCAGCCTGCTGAAGCAGGCTGGAGACGTGGAGGAGAACCCTGGACCTATGGCTCCTAAGAAGAAGAGGAAGGTGATGAGCCAGTTCGACATCCTGTGCAAGACCCCGCCGAAGGTGCTGGTGCGGCAGTTCGTGGAGAGATTCGAGAGGCCCAGCGGCGAAAAGATCGCCAGCTGTGCCGCCGAGCTGACCTACCTGTGCTGGATGATCACCCACAACGGCACCGCGATCAAGAGGGCCACCTTCATGAGTTATAACACCATCATCAGCAACAGCCTGAGTTTTGACATCGTGAACAAGAGCCTGCAGTTCAAGTACAAGACCCAGAAGGCCACCATCCTGGAGGCCAGCCTGAAGAAGCTGATCCCCGCATGGGAGTTCACGATTATCCCTTACAACGGCCAGAAGCACCAGAGCGACATCACCGACATCGTGTCCAGCCTGCAGCTGCAGTTCGAAAGCAGCGAGGAGGCCGACAAGGGGAATAGCCACAGCAAGAAGATGCTGAAGGCCCTGCTGTCCGAAGGCGAGAGCATCTGGGAGATTACCGAGAAGATCCTGAACAGCTTCGAGTACACCAGCAGATTTACCAAAACGAAGACCCTGTACCAGTTCCTGTTCCTGGCCACATTCATCAACTGCGGCAGGTTCAGCGACATCAAGAACGTGGACCCGAAGAGCTTCAAGCTCGTCCAGAACAAGTATCTGGGCGTGATCATTCAGTGCCTGGTCACGGAGACCAAGACAAGCGTGTCCAGGCACATCTACTTTTTCAGCGCCAGAGGCAGGATCGACCCCCTGGTGTACCTGGACGAGTTCCTGAGGAACAGCGAGCCCGTGCTGAAGAGAGTGAACAGGACCGGCAACAGCAGCAGCAACAAGCAGGAGTACCAGCTGCTGAAGGACAACCTGGTGCGCAGCTACAACAAGGCCCTGAAGAAGAACGCCCCCTACCCCATCTTCGCTATTAAAAACGGCCCTAAGAGCCACATCGGCAGGCACCTGATGACCAGCTTTCTGAGCATGAAGGGCCTGACCGAGCTGACAAACGTGGTGGGCAACTGGAGCGACAAGAGGGCCTCCGCCGTGGCCAGGACCACCTACACCCACCAGATCACCGCCATCCCCGACCACTACTTCGCCCTGGTGTCCAGGTACTACGCCTACGACCCCATCAGTAAGGAGATGATCGCCCTGAAGGACGAGACCAACCCCATCGAGGAGTGGCAGCACATCGAGCAGCTGAAGGGCAGCGCCGAGGGCAGCATCAGATACCCCGCCTGGAACGGCATTATAAGCCAGGAGGTGCTGGACTACCTGAGCAGCTACATCAACAGGCGGATCTGAAAGAGGTCGCTGCTGAGAAGACCCCTGGCAGCTCCCGAGCTAGCAGTGAATTTGTCGCTCTCCCTCATTTCCCAATGCTTGCCCTCTTGTCTCCCTCTTATCAGGCCTAGGGCAGGAGTGGGTCAGGAGGAAGGTTGCTTGGTGACTCGGGTCTCGGTGGCCTGTTTTGGTGCAATCCCAGTGAACAGTGACACTCTCGAAGTACAGGAGCATCTGGAGACACCTCCGGGCCCTTCTGSlco2a1-P2A-Cre:TGCCCCTGGGCCTCACCATACCTGTCTCTTCCTGCCTCATAGGTACCTGGGCCTACAGGTAATCTACAAGGTCTTGGGCACACTGCTGCTCTTCTTCATCAGCTGGAGGGTGAAGAAGAACAGGGAATACAGTCTGCAGGAGAATGCTTCCGGATTGATTGGAAGCGGAGCTACTAACTTCTCCCTGTTGAAACAAGCAGGGGATGTCGAAGAGAATCCTGGACCTATGGCTCCTAAGAAGAAGAGGAAGGTGATGAGCCAGTTCGACATCCTGTGCAAGACTCCTCCAAAGGTGCTGGTGCGGCAGTTCGTGGAGAGATTCGAGAGGCCCAGCGGCGAGAAGATCGCCAGCTGTGCCGCCGAGCTGACCTACCTGTGCTGGATGATCACCCACAACGGCACCGCCATCAAGAGGGCCACCTTCATGAGCTACAACACCATCATCAGCAACAGCCTGAGCTTCGACATCGTGAACAAGAGCCTGCAGTTCAAGTACAAGACCCAGAAGGCCACCATCCTGGAGGCCAGCCTGAAGAAGCTGATCCCCGCCTGGGAGTTCACCATCATCCCTTACAACGGCCAGAAGCACCAGAGCGACATCACCGACATCGTGTCCAGCCTGCAGCTGCAGTTCGAGAGCAGCGAGGAGGCCGACAAGGGCAACAGCCACAGCAAGAAGATGCTGAAGGCCCTGCTGTCCGAGGGCGAGAGCATCTGGGAGATCACCGAGAAGATCCTGAACAGCTTCGAGTACACCAGCAGGTTCACCAAGACCAAGACCCTGTACCAGTTCCTGTTCCTGGCCACATTCATCAACTGCGGCAGGTTCAGCGACATCAAGAACGTGGACCCCAAGAGCTTCAAGCTGGTGCAGAACAAGTACCTGGGCGTGATCATTCAGTGCCTGGTGACCGAGACCAAGACAAGCGTGTCCAGGCACATCTACTTTTTCAGCGCCAGAGGCAGGATCGACCCCCTGGTGTACCTGGACGAGTTCCTGAGGAACAGCGAGCCCGTGCTGAAGAGAGTGAACAGGACCGGCAACAGCAGCAGCAACAAGCAGGAGTACCAGCTGCTGAAGGACAACCTGGTGCGCAGCTACAACAAGGCCCTGAAGAAGAACGCCCCCTACCCCATCTTCGCTATCAAGAACGGCCCTAAGAGCCACATCGGCAGGCACCTGATGACCAGCTTTCTGAGCATGAAGGGCCTGACCGAGCTGACAAACGTGGTGGGCAACTGGAGCGACAAGAGGGCCTCCGCCGTGGCCAGGACCACCTACACCCACCAGATCACCGCCATCCCCGACCACTACTTCGCCCTGGTGTCCAGGTACTACGCCTACGACCCCATCAGCAAGGAGATGATCGCCCTGAAGGACGAGACCAACCCCATCGAGGAGTGGCAGCACATCGAGCAGCTGAAGGGCAGCGCCGAGGGCAGCATCAGATACCCCGCCTGGAACGGCATCATCAGCCAGGAGGTGCTGGACTACCTGAGCAGCTACATCAACAGGCGGATCTGACCTTCAGCTGGGACTACTGCCCTGCCCCAGAGACTGGATATCCTACCCCTCCACACCTACCTATATTAACTAATGTTAGCATGCCTTCCTCCTCCTTCCNpnt-P2A-FlpO left homology arm; GATTGAGGTCAGGCCAGAAG and TCGACATCGTGAACAAGAGC for Npnt-P2A-FlpO right homology arm; CTGGTGAAAGGGGAACTCTTGCT and GATCCCTGAACATGTCCATCAGG for Slco2a1-P2A-Cre left homology arm; TACAGCATCCCTGACAAACACCA and TAGCACCGCAGGTGTAGAGAAGG for Slco2a1-P2A-Cre right homology arm.Transfected cells were cultured and resulting colonies directly screened by PCR for correct integration using the following genotyping primers: flanking primer ATGCATTGCTTCATGCCATA and internal recombinase primer CCTTCAGCAGCTGGTACTCC for The inserted transgenes were fully sequenced and candidate lines were analysed for normal karyotype. Lines passing quality control were aggregated with albino morulae and implanted into pseudopregnant females, producing germline-competent chimeric founders which in turn were crossed with the appropriate reporter lines on the C57/BL6 background.All experimental procedures using live animals were performed according to protocols approved by Institutional Animal Care and Use Committees (IACUC) of all participating institutions: Allen Institute for Brain Science, Baylor College of Medicine, Broad Institute of MIT and Harvard, Cold Spring Harbor Laboratory, Harvard University, Salk Institute for Biological Studies, University of California Berkeley, University of California San Diego and University of Southern California. Macaque experiments were performed on animals designated for euthanasia via the Washington National Primate Research Center’s Tissue Distribution Program.Postmortem adult human brain tissue collection was performed in accordance with the provisions of the United States Uniform Anatomical Gift Act of 2006 described in the California Health and Safety Code section 7150 (effective 1 January 2008) and other applicable state and federal laws and regulations. The Western Institutional Review Board reviewed tissue collection processes and determined that they did not constitute human subjects research requiring institutional review board (IRB) review. Before commencing the human Patch-seq, the donor provided informed consent and experimental procedures were approved by the hospital institute review board.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-021-03950-0.Reporting SummarySupplementary Table 1Confusion matrices corresponding to Extended Data Figure 5.Supplementary Table 2Cell type nomenclature.Peer Review File


Deroplatys truncate, D. lobate, Amorphoscelis chinensis and Macromantis sp. belong to Deroplatyidae, Amorphoscelidae and Photinaidae family, respectively. Our results indicated that the ATP8 gene may be lost in D. truncate and D. lobata mt genome, and four tRNA genes have not been found in D. truncate, D. lobata and Macromantis sp. A dN/dS pair analysis was conducted and it was found that all genes have evolved under purifying selection. Furthermore, we tested the phylogenetic relationships between the eight families of the Mantodea, including 35 species of praying Mantis. Based on the complete mitochondrial genome data, it was also suggested as sister to Deroplatyidae + Mantidae, Metallyticus sp., the only representative of Metallyticidae, is sister to the remaining mantises. Our results support the taxonomic system of Schwarz and Roy and are consistent with previous studies.Praying mantises are distributed all over the world. Though some Mantodea mitogenomes have been reported, an evolutionary genomic and phylogenetic analysis study lacks the latest taxonomic system. In the present study, four new mitogenomes were sequenced and annotated.  The praying mantis comprises up to 2,300 species having diversified morphological and ecological characteristics. These insects colonize at wider range of habitats, including arid and tropical rainforests, temperate regions and engaged in multiple hunting tactics , 2. PrayThe mitochondrial genome (mitogenome), as a robust molecular marker , has recAmorphoscelis belongs to the subfamily Amorphoscelinae, which can be distinguished from all other mantis through some characters such as: small, dorsoventrally flattened body, and adapted to a bark-living lifestyle Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: I Don't Know**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: NoReviewer #2: No**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: In the manuscript titled 'Complete mitochondrial genomes of four species of praying mantises  with ribosomal second structure, evolutionary and phylogenetic analyses' the author present the complete mitochondrial genome of four species of mantid. Overall the study is good and should be published but some revisions need to be addressed before publishing.1) The authors need to have a native English speaker review the document for grammar. It is not unintelligible but there are many spots where it is difficult to follow or does not sound scientific.2) Throughout the document, the species D. truncata and D. lobata are converted to truncate and lobate due to autcorrect from their word processor . It is annoying but an easy fix.3) I would like to see a table or tables that list the gene order and position for all ORFs for each species, this generally is a useful reference for other readers.Reviewer #2: The authors sequence and annotate the mitogenomes of four mantises species and combine this new data with existing mantis data to estimate phylogenetic relationships in the group. They find a few missing genes in some of the four new mitogenomes, but tRNA secondary structure and codon usage was similar to other mantises. The phylogeny also tended to agree with current taxonomy.I think the study is definitely a worthwhile addition to insect mitogenomics. I particularly appreciate that you discuss your findings in the context of insect and mantis mitogenomics. However, I do have several issues that should be addressed, some more major then others:1. You need to re-work some of the structure in the Introduction. For example, the second paragraph introduced mitogenomes and then abruptly switches to introducing the taxonomy of mantises. I would separate these.2. Because the Methods comes last, I would include some brief methodological details in the Results and Discussion section.3. Please include more specific results. For example, you vaguely state that there is a high A+T% and list the most-used codons. Please provide specific values in the text.4. You begin discussing the utility of using cox1 as a barcoding gene (starting line 151), but this discussion appears abruptly. Why is this relevant? Is it related to your nucleotide diversity results? Please explain.5. I’m a little confused by your sampling strategy. Did you sample the 35 mantis species mentioned in lines 189-190? Or were these taken from GenBank?6. I am not convinced you are actually missing the atp8 genes. In my experience, it can be difficult to annotate this gene, and you often have to look for it manually based on ORFs, etc. It also seems like there is a non-coding region on the 5’ end of your atp6 genes when a “missing” atp8 is reported. I would bet that is the atp8 gene. Also, were your missing genes confirmed with your PCR step? Finally, it looks like both atp6 and atp8 are missing from one species , but I didn’t see any discussion of this. Please elaborate.7. Did you confirm that these assembled mitogenomes are in fact complete and circular? I recommend doing this, especially since you report missing genes.8. Please provide more methodological details. For example, how did you calculate Ka/KS? What were your PCR conditions? What GBlocks parameters did you use?9. There are several portions of the manuscript that are difficult to read. I recommend going through the text and again and edit for grammar, awkward phrasing, etc.Some minor comments:Line 25: There is no context for referencing the “Schwarz and Roy (2019) taxonomic system.” Perhaps just say “the latest taxonomy.”Line 31: No need to say “respectively.” Same in line 82.Line 42: Be careful about using “bugs” when you mean “insects.” Entomologists will strongly object to this usage…Lines 74-78 should be in the IntroductionLine 100: Why do you rule out this second possibility? Please elaborate.Line 144: Please provide a brief overview of dN/dS ratio .Line 217: Confusing phrasing. Please re-write. It’s not clear what “miniscule to align means.”Figure 1: It’s not clear which mitogenomes go with each species. Please add names to the figure.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  24 May 20214/5/2021Dear Editor, Thank you very much for your email message regarding our manuscript. We appreciated much the valuable comments from the reviewers for revising and improving our manuscript. Generally, we have incorporated all suggestions and comments of yours into this revision. More specifically, our corrections are as follows here under.We hope you can now accept this revised version. Most sincerely,All authorsReviewer #1: In the manuscript titled 'Complete mitochondrial genomes of four species of praying mantises  with ribosomal second structure, evolutionary and phylogenetic analyses' the author present the complete mitochondrial genome of four species of mantid. Overall the study is good and should be published but some revisions need to be addressed before publishing.1) The authors need to have a native English speaker review the document for grammar. It is not unintelligible but there are many spots where it is difficult to follow or does not sound scientific.Reply: We thank this referee for his/her suggestions. We have polished the full text and further revised the corresponding parts. 2) Throughout the document, the species D. truncata and D. lobata are converted to truncate and lobate due to autcorrect from their word processor . It is annoying but an easy fix.Reply: Thank you. 3) I would like to see a table or tables that list the gene order and position for all ORFs for each species, this generally is a useful reference for other readers.Reply: We agree and have added the related information on the position of all ORFs. See Table S2. For the gene order, we refer to Figure 1 with the mitochondrial gene structure.Reviewer #2: The authors sequence and annotate the mitogenomes of four mantises species and combine this new data with existing mantis data to estimate phylogenetic relationships in the group. They find a few missing genes in some of the four new mitogenomes, but tRNA secondary structure and codon usage was similar to other mantises. The phylogeny also tended to agree with current taxonomy.I think the study is definitely a worthwhile addition to insect mitogenomics. I particularly appreciate that you discuss your findings in the context of insect and mantis mitogenomics. However, I do have several issues that should be addressed, some more major then others:1. You need to re-work some of the structure in the Introduction. For example, the second paragraph introduced mitogenomes and then abruptly switches to introducing the taxonomy of mantises. I would separate these.Reply: Agree. We have separated the two parts. Please check the line 53.2. Because the Methods comes last, I would include some brief methodological details in the Results and Discussion section.Reply: Thank you. We have added the brief methodological details in the Results and Discussion section. Please check lines 78-83. 3. Please include more specific results. For example, you vaguely state that there is a high A+T% and list the most-used codons. Please provide specific values in the text.Reply: Agree. We have added the whole genome, A+T-rich region and PCGs specific values of four mitochondrial genomes. Please check Table S2.4. You begin discussing the utility of using cox1 as a barcoding gene (starting line 151), but this discussion appears abruptly. Why is this relevant? Is it related to your nucleotide diversity results? Please explain.Reply: Thank you. The dN/dS pairwise analysis showed that cox1 is undergoing a robust purifying selection (0.028). That is why cox1 can be used as a molecular marker of barcoding entire Mantidae. We also reorganized the writing of this part. Please check lines 152-153.5. I’m a little confused by your sampling strategy. Did you sample the 35 mantis species mentioned in lines 189-190? Or were these taken from GenBank?Reply: We have reorganized the writing of this part. Please check lines 189-193 and Table 1.6. I am not convinced you are actually missing the atp8 genes. In my experience, it can be difficult to annotate this gene, and you often have to look for it manually based on ORFs, etc. It also seems like there is a non-coding region on the 5’ end of your atp6 genes when a “missing” atp8 is reported. I would bet that is the atp8 gene. Also, were your missing genes confirmed with your PCR step? Finally, it looks like both atp6 and atp8 are missing from one species , but I didn’t see any discussion of this. Please elaborate.Reply: Thank you for this advice. We know ATP8 which is difficult to annotate. We repeated to analysis the sequence for annotation of ATP8, and in addition we ran the PCR to amplify the corresponding part of about 1000bp to confirm the sequence. Unfortunately, we could not annotate the relevant homologous sequences. For the ATP6 of Macromantis sp. species, we have added this annotation name in Figure 1  and the Table S2. Please check Figure 1 and Table S2. 7. Did you confirm that these assembled mitogenomes are in fact complete and circular? I recommend doing this, especially since you report missing genes.Reply: We thank this referee for his/her comment. We received the assembled mitogenomes sequence and this was confirmed by PCR technology. The starting and the ending of sequence overlapped with 184bp, so we are sure that this is a complete and circular structure. We redesigned the primers for PCR and sequenced the PCR product to confirm the sequencing results. The primers are listed in Table S1. We also used ORF Finder and BLAST search engines against the GenBank database to identify protein-coding and rRNA genes. Please check the lines 206-208.8. Please provide more methodological details. For example, how did you calculate Ka/KS? What were your PCR conditions? What GBlocks parameters did you use?Reply: We agree and have added the methods of calculating Ka/KS. Please check lines 219-221. We also added the conditions of long PCR reactions. Please check lines 213-217. Also the GBlocks parameters were added in lines 237-239.9. There are several portions of the manuscript that are difficult to read. I recommend going through the text and again and edit for grammar, awkward phrasing, etc.Reply: Thanks for your suggestion. We polished the text and revised the corresponding parts. Some minor comments:Line 25: There is no context for referencing the “Schwarz and Roy (2019) taxonomic system.” Perhaps just say “the latest taxonomy.”Reply: Done; please check lines 26-27Line 31: No need to say “respectively.” Same in line 82.Reply: Done; please check lines 31, 84Line 42: Be careful about using “bugs” when you mean “insects.” Entomologists will strongly object to this usage…Reply: Done, please check line 42.Lines 74-78 should be in the IntroductionReply: Agree. We have moved this text to the Introduction. Please check line 64-68.Line 100: Why do you rule out this second possibility? Please elaborate.Reply: We apologize for this. Indeed the second possibility is also very likely. So, we have reorganized this text part. We have deleted our conclusion: “We concluded that a lack of the atp8 gene in D. truncate and D. lobate is most likely because of the former or the later reason.” Please check lines 112-113. Thank you.Line 144: Please provide a brief overview of dN/dS ratio .Reply: Agree. Please check lines 151-152.Line 217: Confusing phrasing. Please re-write. It’s not clear what “miniscule to align means.”Reply: We thank this referee and have rephrased this part text, which means Nad4L is too short to align among the Mantodea species. ATP8 was not identified in D. truncate and D. lobate. So nad4L and atp8 were excluded in the phylogenetic analysis. Please check lines 238-241. Figure 1: It’s not clear which mitogenomes go with each species. Please add names to the figure.Reply: Thanks and done.AttachmentR1-rebuttal letter-final.docxSubmitted filename: Click here for additional data file. 25 Jun 2021PONE-D-21-03889R1Complete mitochondrial genomes of four species of praying mantises  with ribosomal second structure, evolutionary and phylogenetic analysesPLOS ONEDear Dr. Smagghe,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.Minor issues were raised which can be considered since they will improve the manuscript somewhat.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Aug 09 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). 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If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #2: (No Response)**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #2: Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #2: Thank you for your thorough responses and edits to your manuscript based on my comments. I think the manuscript has been improved. I do have a few comments that still should be addressed, based on my first round of comments:1. I still think the paragraphs in the Introduction are a little disconnected. Perhaps switch paragraph #2 (on the utility of mitogenomes for mantid phylogenetics) and paragraph #3 (on mantid taxonomy).2. I think you should add the AT% to the main text, not just in a supplement.3. I’m still somewhat skeptical that the atp8 gene is actually missing, but your combination of NGS and PCR results are more convincing. It is difficult for me to fully evaluate this claim without access to the sequence data; but as is stated in the manuscript, atp8 and other genes are missing from other taxa. Is this the first case of missing mt genes in mantises?4. I recommend one more round of editing for writing clarity. For example, Lines 50-51 are a little confusing, and should probably be split up into two sentences.**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  27 Jun 202126/06/2021Dear Editor, Thank you very much for your email message regarding our manuscript entitled “Complete mitochondrial genomes of four species of praying mantises  with ribosomal second structure, evolutionary and phylogenetic analyses”. We appreciated the valuable comments from the reviewers and editor to revise and improving our manuscript. Generally, we have incorporated all suggestions and comments into this revision. More specifically, our adjustments are stated below in a point-by-point manner.We hope that this revised version can be accepted for publication. Most sincerely,All authorsReviewer #2: Thank you for your thorough responses and edits to your manuscript based on my comments. I think the manuscript has been improved. I do have a few comments that still should be addressed, based on my first round of comments:1. I still think the paragraphs in the Introduction are a little disconnected. Perhaps switch paragraph #2 (on the utility of mitogenomes for mantid phylogenetics) and paragraph #3 (on mantid taxonomy).Reply: We agree and have switched the paragraph #2 and paragraph #3. See lines 46-68.2. I think you should add the AT% to the main text, not just in a supplement.Reply: Indeed, we have added the AT% information to the main text; see in Table 2.3. I’m still somewhat skeptical that the atp8 gene is actually missing, but your combination of NGS and PCR results are more convincing. It is difficult for me to fully evaluate this claim without access to the sequence data; but as is stated in the manuscript, atp8 and other genes are missing from other taxa. Is this the first case of missing mt genes in mantises?Reply: Thanks for your suggestion. Here we attach mitochondrial sequencing results for D. truncate species, and we really did not identify the atp8 gene. So, we imply that atp8 gene may have been lost. Yes, from our sequence of D. truncate and D. lobata, we can confirm the atp8 is lost. This is the first case of missing Mt gene in mantises. TAATTATCCCATGAAAGATTAGTATTAAACCAATAACTCTAAATTTATTTTAAATAATAGAGTAAAATGCCTGATTTAAAAGGAATATCATGATAGGATAAAATATGTAAATTAATTACTTTTACTAATATTATCATTACACATAACAGAGTTAAACTGTTTCCTAAAATATCAAAAATTTTTGTGCATCCTACACTAAAATATAGAAAAGTAAGCTAAATTTAAGCTAATGGGTTCATACCCCATATATAGAGTTTACACTCTCTTTTCTAGTGCCCAACAATTCGATAAAAATCCTCTTTTTAATAACTTTAATTAGAGGTGTGTTAATTTCATTATGTGCTAATTCATGAATAGGAGCATGAATAGGATTAGAAATTAACTTACTCTCCTTCATTCCTTTACTTTCTTCCAGAAAAAACTTGTTCTCAACTGAAGCTTCACTTAAATATTTTCTTATTCAAGCTATTTCATCATCTACATTTCTATTTCTAATTATGATAAAAATTAATATCCAAGAAATGTTTTATCTAATAAAATTTAATAATTGAAACACCTTAATTACAATTCCTATTCTAATGAAAATTGCATCTGCACCTTTTCATTGATGATTACCTTCCGTGATAGAAGGTTTATCTTGAATAAATTGTTTTATTATTCTGTCAATTCAAAAAGTAGCCCCATTAATATTCATTTCCTACTTAATCACTAATAACTTCTTTATTCAAATCATCATTACAATTTCTGCAATTATAGGAGCTATTGGAGGTCTCAATCAAATTTCTCTACGAAAAATTTTATCATTTTCATCTATCAACCATATTGGATGAATATTAACAACCATAATTATAGGTTCTAACTTTTGAATAACATACTTTATCATTTATACGGTAAATATTATCCCTATTATTTTTATGGTAAACAAAATAAACCTATCTTTTATTCCTCAGACCTTTAATTCCTTCAATAACAAAAAAATTATTAAATTCATTCTATTTATCTCCCTCCTTTCTTTAGGGGGCCTCCCTCCTTTTATTGGTTTTTTCCCAAAATGAATTATCATTCAAATTATAATCCAAAATTTAATAATTCTTACATCAATAACCCTAATTATATCCTCCCTTTTAACCCTTTATTATTATCTACGAATTATTTACACAACATTAATAATCACAAATTCAGAAATAACTTGAATAGCAATCTACTCAAATAACAACTTAGGAAAAAGAACATTTCTTTTCTTATTCATTCTTATTTTTGGTTTATCAATCTGTACACTTATCTTAACAATCTATTAAGATTTTAAGTTAACCAAACTAATATCCTTCAAAGTTATAAATAAAGTGATTCATCTTTAAGTCTTAGTATTACTTACACCTTTAGAATTGCATTCTAATATCATCTCCATGAATATAAAACTTTAGTAAAAGAGATAATAATCTCATTAATAAATTTACAATTTATTACCTAATATCAGCCATTTTACTTTTTTTTTTGCAACGATGATTATTCTCAACAAATCATAAGGATATTGGAACATTATATTTTATTTTTGGTGCATGGGCAAGTATATTAGGAACATCTTTAAGAATTTTAATTCGAACAGAGTTAAGACAACCCGGATCTTTAATTGGAGATGATCAAATTTATAATGTCATTGTAACAGCTCACGCTTTTATTATAATCTTCTTTATAGTTATACCCATTATAATTGGTGGATTTGGTAATTGATTAATTCCTTTAATACTTGGAGCTCCAGATATAGCCTTTCCTCGAATAAATAATATAAGATTTTGATTATTACCTCCTTCTATTCTATTTCTTATTATCGGAAGAGTTGTAGAAAGAGGAGCTGGTACAGGATGAACAGTCTATCCCCCTCTTTCAGCCAGAATTGCTCACGCCGGCCCCGCAATTGATTTAACCATTTTTTCCTTACACTTAGCTGGAATATCAAGAATTATAGGAGCTGTAAATTTTATCACAACTATAATTAACATAAAATCAATTCATATAAATCATACTCAAATTCCTTTATTTGTTTGATCTGTAGGAATTACAGCAATTCTTCTTCTTTTATCTCTCCCTGTACTTGCTGGGGCAATCACTATACTTCTAACTGATCGAAACTTAAATACATCCTTTTTCGACCCTGCTGGAGGGGGGGATCCTATTCTCTATCAACATCTATTTTGATTTTTTGGACATCCTGAAGTATATATTTTAATTTTACCAGGATTTGGTATAATTTCTCATGTCATTTCTCATGAAAGAGGAAAAAAAGAAGCTTTTGGAAATTATGGAATAATTTGAGCTATATCAGCCATTGGTTTTCTGGGGTTTATTGTATGAGCTCATCATATATTTACAGTAGGAATAGATGTAGATACACGAGCCTACTTTACAGGAGCTACTATAATTATTGCTGTTCCTACGGGTATTAAAATTTTTAGTTGACTCACAACTATATACGGAACAAAAATAATTTATAGTGTAGTATCTTTATGAGCATTAGGATTTATTTTTTTATTTACAGTTGGAGGTCTTACAGGAGTAATTTTAGCCAATTCAGCTATTGATATTATTCTTCATGACACTTATTATGTAGTAGCCCATTTTCACTATGTACTTTCAATAGGAGCCGTTTTTGCTATTATAGCAGGGTTCATTCATTGATATCCCTTATTCACTGGATTATCATTAAATCCTAATTGATTAAAAAGTCAATTTTTCACAATATTTGTAGGAGTTAATTTAACATTTTTTCCACAACATTTCTTAGGATTAGCTGGAATACCTCGACGCTATTCAGATTACCCTGACGCCTATAGATCATGAAACTTCTTATCATCTGTAGGAGCAATAATTTCTTTCGCTGCTGTTATCATATTCATTCTTATCTTATGAGAAAGAATTACCTTAAATCGATTTATATTATTTTCATCTCAAATAAATAGATCAATTGAATGAATTCATAATTTTCCCCCAGCTGAACACACCTACAATGAACTAACTCTAATTACAAATTAAAACCTAAATTGTTGATAATTTTCCTCATTTCTAATGTGGCAGAATAGTGCACTGGGTTTAAGCTCCAAAAATAAAGATAAACTTTTTTTAGAAGTTATTTTAATGGCTACAAATGCAAATTTAGGATTTCAAGATAGAGCCTCCCCCTTAATAGAACAACTCATGTATTTTCATGATCATTCCATATTTATTATTACTATAATTGTAATTACAGTAAGTTATATAATTATAGCTTTAATAGTAAATAAATTTTCTGATCGTCATGTTATAGATGGTCAATATTTAGAAATTTTTTGAACAGTTCTTCCAGCCATAGTTCTAGTCTTCATCGCTCTACCTTCTCTACGGATTTTATACCTAATTGATGAAAATACAAATCCAACATTAACCTTAAAAACAATTGGTCATCAATGGTATTGAAGTTACGAATATTCAGATTTTACCAATATTGAATTTAACTCTTACATAATTCCACAAAATGATTTAAATCTATTTAACATGCGTTCACTTGAAGTTGACAATCGAACATCATTACCCATAAATACCTTAACACGAATTTTAATCACCTCAGATGATGTTATTCATTCTTGAACAATTCCGAGAATTGGAGTAAAAGCTGATGCTACTCCGGGACGATTAAATCAAGCAACATTTTGATTTAATCGTCCCGGAGTATTTTATGGTCAATGTTCAGAAATTTGTGGAGCAAATCACAGATTTATACCTATTGTAATTGAAAGAACTTTGATTAATAATTTTCTTAGTTGAATCTTAAATTATATTGAATCACTAGATGACTGAAAATAAGTGATGGTCTCTTAAACCATATCATAGTAACATAATAACTACTTCTAGTGATTGACTAACAATTTATCAAGAAGTTAGTTAAAAAATAACATTAGTATGTCAAACTAAAGTCATTATCATTTAATACATCTTTATACCCCAAATAATACCCCTAAATTGGCTAATCCTATTTTACATTGTTTCTACTAGATTAATTTTTTTTAATGTAATAAATTTTTTTATATTTTCCCACAAAATCCCATTAACTTCAAATAAAATTTTACTAAAAACCCTAATTTGAAAATGATAACCAACCTATTTTCAATTTTTGATCCCTCTTCAAATTTTATAAACCTATCAATAAATTGACTTAGAATTTGAATCGGATTATTATTATTTCCTTCCTCGATATGGTTAATTTCATCACGAAATAAAACCCTTTGAAGTTTTATTTTAAGTAAACTTCATGAAGAATTTAAATTATTAATTGGTAAAAAAAAAATTAACAAAGGATCAACATTCATATTTATTTCAACATTTTTACTTATTATATATAATAATTGTATAGGATTATTTCCATATATTTTTACTGGGACAAGTCATATAGCTATAACTCTATCTTTTGCTTTACCTTTATGACTAAGATTTATACTCTTTGGTTGAATTAATAACTCTAATCACATATTTATTCATTTAGTTCCTCAAGGAACTCCAAATATATTAATACCTCTTATAGTTTGTATTGAAACAATTAGAAATTTAATTCGTCCCGGAACTTTAGCTATTCGACTCGCAGCAAATATAATTGCAGGACATTTATTAATAACCCTCCTAGGAAATTCTGGGAGAAACATTATAGATTCATTTTTACCCCTATTAATTTTAGTTCAAATTATACTTTTAACTCTAGAATCCGCAGTTGCTATTATCCAATCATATGTTTTTGCAGTATTAAGTACTTTATACTCTAGAGAAGTAAATTAATAATGATAATACACACTAATCACCCCTACCATTTAGTTACTTATAGTCCCTGACCTATTATAACTACTTTAAGAATCATAATCATAATATTAGGTTTTATTAAATTTTCTTATGAGTTTAGTGAAAAATTTATGCTATTAGGAACTTTAATTTTAATTTTAATTACTACTCAATGATGACGTGACGTTGTACGAGAAAGTACATATCAAGGATTACACACTAAAAAAGTAATCTTTGGACTACGATGAGGAATAATTTTATTTATTATTTCAGAAATTTTTTTCTTTGTATCTTTTTTTTGAACTTTTTATCATAGAAGCTTAACTCCTACTATTGAATTAGGGTCCTTTTGACCACCTCAAGGAATCTGACCCTTTAATGCTCTTCATGTTCCTCTTCTTAATACAACGGTACTTTTAGCATCAGGCATCACTATTACATGAAGTCACCATGGACTATTAATAAATAATTATAATCAAGCCACCCAAGGATTAATATTTACCATTATCCTTGGGATTTATTTTACCATCTTACAACTCTATGAATATTATGAAGCTCCGTTTACAATTGCGGATTCAGTTTTTGGGTCAATCTTTTTTATAGCAACTGGATTTCATGGACTTCATGTAATTATTGGAACTACATTCTTAGTTACATGCTTATTTCGAATAATTTATAAACATTTTACATCTATTCACCACTTTGGTTTCGAGGCAGCAGCCTGATATTGACATTTTGTGGACGTAGTATGATTATTCCTGTACATTTCTATTTATTGATGAGGGAGATAAATCCAATTTATTTAGTATAAAAGTACAATTGATTTCCAATCAAAAAGTCTATATTTAATTAGAATAAATAATTAAAATTTTAATTTTTATCTCATTCATTACTATATCAATTACCTTAACAATCATATTACTAACAAATTTCTTGTCAAAGAAAAAAATTGAAGACCGAGAAAAAAATTCACCTTTTGAATGTGGATTTGATCCGATTAGATCCTCGCGCCTTCCCTTTTCTTTACGTTTCTTTTTGATTGGAGTAATCTTTTTAATTTTTGATGTAGAAATCGCCTTTATCTTACCAATAATTATCATTCCTCTTACATCAAAAATAACATCTTGAATATCTACTAGAATTATATTCTTATTGATCTTAACAACTGGTTTATTTCATGAATGAAATCAAGGTTCTCTCGACTGAGCAACTTAACTTTATAAGGGTTATAGTTAAAAATAACATTTGACTTGCACTCAAAAAGTATTGAAATATCAATTTTCCTTATTATAAGTAAGAAGCAAATTCATTGTAATCAGTTTCGACCTGATAGTAAGATATTCATATCCTTATTTGTTGATTAATTGAAACCAAATAGAGGTATATCACTGTTAATGGTAAAATTGAAATTAATGCTTTCCAATTAAGAAAATGTGTAGATCGAATATAAGTTGCTAATTTATTATTCAAGTGGTTTAATCCCATTTACATTTTAATTTATATAGTTTAAATAAAACATTACATTTTCATTGTAAAAATAAATTTTTCAATTTTATTAATAGTAAGTACTCTATTTAAAGATAAATTAGTTATCTCAATAACAGCTTCAATGTTATACTCTCTATAAGATATTTAAATAAATACAAAATATTATTATAAGTAAAATACTAAAAAAAATCATTAAATAAGATTTTAAATCATTATATTGAAATCATTGATTAAAACATCTTAATTTTATCAAAATATAATATAAATTTTGTGCTCCAAAATATTCTCTTCAGCCTAAATCAATATATATCATTGAATTTAAACTAATAATTAAAGGTATTTTTCTTACTCCTTTAGTAAAAATTAAAGGTATATATCATATTGAAGCAAAAAAAATTGTCATTTTATAATACTTAAATGTTGAAAAATAATAATTTATTCTATACTTAAATATAATTCTTCCTAATCATAAACCTACAAATCTACCTAATAAAGGTATTATTTTCATTATTAATGTCATATAAATTATATAGGGAGTAAGAAAAATAATCCAATTTAATATCCCACCTCCAATAATTGCAAATAATATTAAACCTAATATTCCATATACTATTATTCAACTTTCATTTAATTTACATATTGATATTATATTAAAATCTCCTCATAATACATAATAGGATAAACGAAAAGAATAACTTACTGTTAAACCTGTTGAAAAAAAAAATAAAACATATATAAAGATATTTAAATTTCTTAAAGATAATATTTCTAAAATTATATCTTTTGAATAAAATCCTGCTAAAAAAGGTATACCACATAAAGCAAAATTTGATACTATAAAACAAGATGAAGTAAAAGGCATAAATATTACTAAATTTCCTATAAAACGAATATCCTGTAAATTTTTTATTCTATGGATTATTATTCCTGTACACATAAATAACAATGCCTTGAATAAAGCATGAGTTAATAAATGAAAAAAAGCCAAATCTGAAAAACCTAATGATAAAATTCTTATTATTAATCCTAATTGACTTAAAGTTGATAAAGCAACAATCTTTTTTAAATCATATTCAAAATTTGCTCCTAACCCTGATATAAACATTGTTAAAACAGATATTACTAGTAAAAACTTTAATAATCAATCCGGAAAAACCTTACAAAATCGGATTAATAAATAAACCCCAGCCGCAACCAAAGTTGATGAATGAACTAGGGCTGAGACAGGAGTTGGAGCAGCTATTGCTGCAGGCAATCAAGCTGAAAAGGGAATTTGTGCACTTTTTGTTATTCCTGCTAAAAGTACTAAAAATGAAATTAAATATATTTCAATTTCATTTAATGTACAATCTAAATAAAAAATATAATTTCATCTTCCAAAATTTAATATTCATGAAATTGCTATTAATAAAGCAACGTCCCCTACTCGATTAGATAAAGCTGTCAATATTCCAGCATTATAAGACTTAGTATTTTGATAATAAATTACAAGACAATAAGAAATTAAACCTAACCCATCTCAACCTAATAAAATTCTAATCAAATTTGGACTAATAATTAAAAACATTATAGACAATACAAATATTAATACTAAAAAAATAAACCGATTTAGTGATGAATCTCCAGTTATATAATCTTCTCTATATAAAATTACTAAGGATGAAATTAATAAAACAAAACTCATAAATATGAGAGACATTCAATCCAACAATATTGTTATAACAACTGAAGAAGTACTTAATCTAACAATTTCTCATTCAATAAAAATAATTAAATCATTTAAAATAAAAACCATTCTAGAAATAAATATTATTAAAGAAAATAATGCTAATAAAAAAAATCTAATAAAACATAATGATAAATAAACCATAACTTAAAATAACTATTCATTATATCCATGATACCACAAATCATAATTTTATGATAAACTATTTAAGTAAATTAACTTTTATTTAAAATCAAATTAAAACATAATCTCTCTTTAAAATTAATAAATTTAAGGGTAATCAATGAAGTATTATTAATAAATATTCACGTCTATACCCTCCAACTCTTCTATAAATGCCTGAATAATAAAATCCATGTTGACTATATGAATATAAATATAAAGTATAAGCAGCTCTAAAAAATGAAATTAATATAAGAAAAAATATAGATATTCATACCCAACTTACTATACTATTGAATAATCCAATTTCTCCTAATAAATTTAAAGTAGGGGGAGCAGCTATATTGCTAGATGAAAGTAAAAATCATCATAATGTTAAACTTGGCATCAAATTTAATAAACCCTTATTAATTAACAATCTTCGTCTTCCCAGACGCTCATAACTAATATTAGCTAAACAAAATAAACCAGATGAACATAAACCATGAGCAATTATTATTACAAAAGTTATATAAAACCCTCAAATATTTAATGTTATTAAGCCCCCAATAATTAAACCTATATGAACCACAGACGAATAGGCAATTAATGCTTTTACGTCTATTTGACGTAAACACATTAAACTAACTATAACCCCACCGATTAATCTAGTAGAAAATCAAAATACATTAACTAATATCCCAACTCTCTTCAAGAATTCATATACTCGTAATAAACCATAACCTCCTAACTTTAATAAAACTCCAGCTAAAATCATTGAACCTGAAACTGGAGCTTCAACATGAGCCTTAGGTAATCATAAATGCACTAAAAATATAGGCATCTTAATCAAAAAAGCTAAAATTATTCTAAAATATAAATATCAATTCAATAAATTATTTTTATAAACTAACGAAAAAACTAAATAGCCCATGTTGTTATAAAAATATATAATACCCATCAATAATGGTAATGAAGCTAAAAGAGTGTATAATAATAAATAAATACCAGCTTGCAATCGCTCAGGTTGATATCCTCACCCAAAAATTAAAAATAAAGTTGGAATCAAACTACCCTCAAAAAAAAAATAAAATGAAATGAAATTTATTCTACAAAAAGTACAAATTAATATCAATAACAATATTAAAATTATAAATATAAATAAATTATTATAAAATTTATATCGAATAACAGAATAACTAGCTAAGATTATTAAAACACAAATTCAAAATCTTAGCACAATAAGACCATATGATAAGTAATCATATCCAAATATATAACTCATTCTTATTCAATAAAAAAAATCCACTCCAATAAAAAATTTAAAAGATATAAAAAATAATAAATTCTGAATTAATAATCAATTTTTATTCATTAAACATAATGGAATCAAAAACATTAAACTTAAAATATATCTTAACATTGCAATAAGCTAAACGAATTAAAATAATCATTTCCATGAGTTCGAATTATAGAAACCAAAATTGATAATCCTAATACACCTTCACAAACTGCAAATGAAAGAAAAATTATAGTCATATATAACTCTCCTCTTATTATTAAATAAAAATACAAAATAATGAACAAAACTAAAACAATAAATTCTAAACTTAATAAAGTTACTAATAAATGTTTACGTCCAGAAGAAAAAACTCACAAACCACATAAAAATATAAAACAGAATATTATTAACATTTTTTAGTTTTAATAGTTTACTAAAAACACTGGTCTTGTAAACCAAAATTAAGAATAATTACTTTTAAAACTTCAAAGGAAAGAAACTCATCATTAATTCCCAAAATTAATATTTTATTATTAAACTACCCTTTGATATTTTATACTCATTATTAACTATAAGTTTAATTTTAAGAATTACTTTGATATTTTTAAATCATCCAATATCTATAGGTTTTATCCTTTTTATTCAAACGTTATGCTTATGTTTTATTAGAGGTTTTATATCACTAAGATTTTGATTCTCCTATGTATTATTATTAATTTATTTAGGAGGAATATTAATTTTATTTATATACATTACCAGATTAGCCTCAAACGAATTATTTTCTTATTCAAATAAAATCTTATTAATTATTTTTTTAGTACCCCTTATCTTAAGACTTATCCATTATACAAGTTTTTATCATCAAACAAACTTATATGAAAATATAGAAAATAGAATAAATTTCATCTTTATACCAAACAACTTTTTATTAAAGATATATAATTATCCTAATAACATTATTACAATCTTAATTGCCTGTTATTTATTTTTAGCTTTAATTGCAGTTGTTAAGATAACAAATATTTTCAAAGGACCCCTACGACAAATAAATTAATAATGTTTAAACCCTTACGAAAAACTCACCCTATCATCAAAATTTCCAATAATGCCTTAGCAGATTTACCTTCACCATCAAATATTAGATCATGATGAAATTTTGGTTCCCTTCTAGGCTTATGTTTAATTCTTCAAGTAATTACAGGACTATTTTTAGCTATACACTATTCAGCTCATATTGATTTAGCCTTCTCAAGAGTGATTCACATTTGTCGAGATGTAAATTATGGGTGACTTTTGCGAATTCTTCATGCTAATGGTGCTTCAATATTTTTTATTTGTATTTATCTCCATATTGGACGGGGGATATATTACGGATCATATAAATTTTATTATACATGAATAATTGGAGTAATAATTCTATTTTTATTAATAGCGACAGCATTTATAGGTTATGTATTACCATGAGGTCAAATATCATTTTGAGGTGCCACAGTAATTACTAATTTATTATCTACTATTCCCGGATTTGGAAATGAATTAGTTCAATGAGTTTGAGGGGGTTTTGCAGTAGATAATGCTACCCTAAACCGATTTTTCACATTTCATTTTATTTTACCATTTATTATTGTAGCAATAATTGCAACCCACTTATTATTTCTTCACCAAACTGGATCAAATAATCCCTTAGGTACTGATAGTAATATTGATAAAATTCCGTTTCACCCTTATTTCACATTTAAAGATATCTTAGGATTCATTATATTATTAACACTTCTATCCCTCTTATCTCTTAAAGAACCCTATATCCTAGGAGATCCTGATAATTTTATTCCTGCTAACCCTCTTGTTACTCCAGTCCACATTCAACCAGAATGATACTTTTTATTTGCTTATGCTATTTTACGCTCTATCCCTAACAAGCTAGGAGGGGTGATTGCTCTAGTAATATCTATTATAATTCTAATTATTATACCTTTCATAGATACTAACTTACGAAGATTTCAGTATCATCCTATTAATCAATTCATATTTTGATTTATAATTATAACTATTACACTTTTAACATGAATTGGAGCACGCCCTGTTGAAGACCCTTTTATCCTAGTAGGACAAATTCTCACCATTATATACTTCCTTTTCTATATCATAAACATTTTAATTATCAAGGTATGAGACCATATTATACAATAATAAATAAGTTATAAACTTAATGTTAAGATTATGTCTTGAAATCATAATAAAGGGGTTAAATTCCCTTATTAACTTTACTTCATCCAACCCTTAAAATAAAATTTTTAACCCTAAATAAAAAAACAAAAAATTTAAAGATAAAGGTAAAAAGCTTTTTCACGCTAAATATATTAATTTATCATATCGATATCGAGGAAGAGTTCCACGAACTCAAATATATATAAAAGCAATAAAAATCAACTTCAAATAAAATACAAATGAATCCAAATTAGAACCCAAAAAAATTACACATATTAACATTCTTATAAATAAAATTCTTGAATATTCTCCTAAGAATATTAATGCAAAGCCTCCCCCTCCATACTCAATATTAAAACCTGAAACTAATTCTGACTCTCCTTCAGCAAAATCAAAAGGTCTACGATTTGTTTCTGCTAAACATGACACTATTCACACATTACACAATGGTAAGTATAAAAATAAAAATCAAATATTCATCTGATAATAAAAAAAATCCATTAATGAATAACTTCCAATTAAAAAAACAAAAGATAATAAAATTAAGGCTAATCTTACCTCATAAGAAATTGTCTGAGCAATTGCTCGTAAACTCCCTAATAAAGCATAATTAGAATTTGAAGATCATCCAGCTAACATAACTGCATAAACACCTAAACTTGTACATACTAAAAAAAACAACAAACCCAAATTAAAAGTAAATAAACCTCTTATATAAGGAAATAATATTCATAAGATTAAAGATAAAAATAAAGTAAATATTGGACATACATAATATAATGAATAATTCGAAACTAAAGGATTTATATATTCCCTACAAAACAACTTTACAGCATCACTGAATGGTTGTAAAATACCAATAAATCCAATCTTATTAGGACCCTTACGAATATGAATGTATCCTAAAACCCTACGTTCTAATAAAGTAAAAAAAGCTACCCCAACTATAACAAAAATAACTAAAATTAACCCTACTAATATTATTAAAAGTACTTCACTAATTAACATTTACTATCTATAATTCAATTTATCTATTTAGGTTCTAAACCTATTACACTTTACTCTGCCAAAATAGTTTTATTAATAATATTCTTTTTCCTTCATAAAAATTATTTCAAATATTTGGTCCTTTCGTACTAAAATATTCTAATTAATTAAAGATAGAAACCAACCTGGCTCACGCCGGTTTAAACTCAGATCATGTAAGAATTTTAAGGTCGAACAGACCTAGTTATTAAACATCTACATTCAAAACTAATCTTAATCCAACATCGAGGTCATAATCTTTTTTTTCAATATGAACTTTCAAAAAAAATTATGCTGTTATCCCTAAGGTAATTTAATCTTCAAATCATTAACCATGGATCTTAAAACTTAATATAATATTAAACTCAAAAAAGAGTTCATTTTATCTTTTAATCACCCCAATTAAATATATTTCACATAGATAATAAATTCATTATTTTAACTTTATTAAATTCAAATATATATTAAACTCTATAGGGTCTTCTCGTCCCTTAATTTTATTTAAGTATTTTTACTTAAAATCTAAGTTAAATTAAATTATTATAAAATAGTCTTTACTTCATCCAACCCTTCATACTAGCCCTCAATTAAAAGACTAATGATTACGCTACCTTTGCACAGTCAATATACTGCGGCCCTTTAAACCACATTCAGTGGGCAGGTTAAATCTCTTATTTCCCCAAGAAACCATGTTTTTAATAAACAGGTGAGTAAATTATTTGCCTAATTCCTCATATTAACATTACATTTCATTCACAAAATAATCCAGACTTTACTAATTAAATCATACTTACTCACTTTTTTTTATTTAACTTAACATTTTAATATACATACTAAATAATAACTAAAAATTTTAAACAAAATAAGAATTAAATTTCAACATCCTTAAATTAATAACAAATTCTAAATTTATACAATTCTTCATTAATAAATTATTATAAATATTCTTCAAAAAGATTTTCCCTTTTAAAGTCAAGAAAACAAAATATTTAAATATTTAAATATTTAATAAATTATATTCTCTAAATTAAATCTATTTATTAAAAAACTAGATATTATTGAAAACGATTAACATTTCACTACTAATTAATAATTCTTAATATTTATAATACAATAATTAAAACTATTAATTAAATCTTTCAAATTCAAGAAAAAAAAAATTAATTATTAATTTCGATATACTCTGAAACACAAGATACAATAAAAAAAATCAACTTAAATTAATTATATAATATTAAACTTCTTTTACAATACTAATTTACTATAGTATTAATTATTATCTCAATTGATAATACAATAACAAAATTTTAATTAATTTATATTCTAAATAATATCAAATCAAAAACAATCTTATGAATTTCAAATTATATTGAATTGCACAATAATAATTTTCAATGTAAATGAAAATCCTTACCTTTAAGTTTAATTTGTTCAAGTGACTTTCTAGGCACACCTTCCGGTACGCCTACTTTGTTACGACTTATCTCATCTTAATATGAGAGTGACGGGCGATGTGTACATATTATAGAGCTATTAATCATTTTAACAAACTTCATAAAATTACAATTAAATCCACCTTTAAATTACTTTCCAATAATTATCCATAATAAATACAATTTATTGTAGTCCATCATCAAACTTACTTATTACTGCACCTTGACTTGAAATATTATATATTTTTATGCTCAGAAAATTATCTAAAAAAATATTCTCAAACAGCGGTATACAAAAATATAAAGTAAGTAAGGTCCAACGCGGATTATCAATTAAATAATAGACTCCTCTAAATAGGTTATAATACCGTCAAATTCTTTAAGTTTCAAGATCATATCTGCTAATACTTTAGTTTATTAATTTACCATAAAAATAATAGGGTATCTAATCCTAGTTTACCTTTTTAATCTCATAATCCATCATACTAACTAATAATTAATTAAAATATTTTAAAAACATTTCACCTTAAAATACTTTAATCATCATTTAACATTAATTAACATTAATTATTTAAAAACCAATATTATTTATTTAATTTCACTGTATCACCGCGGATGCTGGCACAATTTTTACCAAACTTTTATAACATTCACTAAATCTAAAATAATTTAATATCAATAAAATTACCTACTGCACAATTAACTTTATAAACCTCATTGAGAAAACTATAATATACACACATTCACATTCAGAATAACATTAAAACAAATAATCAAAGCAAGAATAAAACTAAATATTTTTTCCAAAAATCGAAGACCTGACATTGGAACACCTAACTTTTTTAATGATTTTTGTATACCTACAAATATTTGATTAAATTAATCATTGCTTCAATATATAAACTTTCAAAAAAAAAATCCACTTTACCCAAAAAATAAGTATGCTTTTTTTAAAACGTTTTCCTCCCTAGAAGTAGGAATTAAGTGCCCAATTCAATTCGATTTAATGTTTTTTTTTAAACATTGGTCTACTAAAGTATATTTAGTATATAATAAATTTAATTATATATATGTCTTATCAAAGATTAATAAAAAAAATATTCTCAAACAGCGGTATACAAAAATATAAAGTAAGTAAGGTCCAACGCGGATTATCAATTAAATAATAGACTCCTCTAAATA4. I recommend one more round of editing for writing clarity. For example, Lines 50-51 are a little confusing, and should probably be split up into two sentences.Reply: Thanks for your suggestion. Lines 50-51 have been split up into two sentences. Please check lines 56-57.AttachmentR2-Reply letter-GS2.docxSubmitted filename: Click here for additional data file. 7 Jul 2021Complete mitochondrial genomes of four species of praying mantises  with ribosomal second structure, evolutionary and phylogenetic analysesPONE-D-21-03889R2Dear Dr. Smagghe,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Ben J Mans, PhDAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 21 Oct 2021PONE-D-21-03889R2 Complete mitochondrial genomes of four species of praying mantises  with ribosomal second structure, evolutionary and phylogenetic analyses Dear Dr. Smagghe:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Ben J Mans Academic EditorPLOS ONE