Biology subjects

Scheuermann, R. H.

Publications and source records attributed to Scheuermann, R. H..

At least 19 recordsLinked to original sources

bioRxiv2026

Limitations of differential expression for cell-type marker discovery in single-cell RNA sequencing atlases

Cell-type marker genes are definitional characteristics of a cell type, and in single-cell RNA sequencing atlases they are commonly nominated through differential expression (DE) testing. The Wilcoxon rank-sum test is the default method for marker gene discovery in Seurat and a recommended method in Scanpy, two widely used computational software packages for scRNA-seq analysis. Structurally, Wilcoxon's standardized statistic grows with sample size at a fixed underlying effect, meaning computed rankings across clusters of unequal size are incomparable. We quantified this effect across three atlas datasets - lung, kidney, and brain. Mean Wilcoxon score scaled with log cluster size at Pearson correlation r = 0.90-0.95 in each organ. In contrast, a classification-based comparator, NS-Forest, showed low correlation with cluster size (r = 0.10-0.28). This DE-cluster size relationship remained essentially unchanged after controlling for classification performance through partial correlation, decayed monotonically under cluster size downsampling, and was reproduced in simulation with marker quality fixed. The Wilcoxon rank-sum test can be transformed into a rank-based effect size by normalizing by the target-background pair count, removing the positive size dependence. However, this effect size nominates an identical set of candidate genes as the original statistic, indicating the cluster size dependence and poor specificity of DE nominated genes are separate problems. Genes nominated under default settings were substantially less specific to their target clusters, with median on-target fractions of 0.204 (lung), 0.166 (kidney), and 0.083 (brain), compared to NS-Forest's marker genes (0.580, 0.687, 0.364), revealing the relatively poor specificity that highly scoring DE genes exhibit.

bioinformatics↗

bioRxiv2026

An Integrated Multi-omics Single Cell Atlas of the Human RPE and Choroid

The retinal pigment epithelium and choroid are critical for supporting the function and maintaining the homeostasis of the outer retina, and their dysfunction underlies a range of inherited and complex ocular diseases. To comprehensively characterize the cellular, transcriptomic, and epigenomic heterogeneity and dynamics within these tissues, we assembled an integrated multi-omics reference atlas comprising 719,813 single-cell/single-nucleus transcriptomes and 234,007 snATAC-seq profiles from 102 ancestrally diverse donors spanning 0 to 99 years of age, including cells from both the macula and periphery. This atlas resolves 48 distinct cell types or states and catalogs 448,567 open chromatin regions. Specifically, we resolved five distinct RPE subpopulations organized along a central-to-peripheral spatial axis, alongside two distinct stress/senescence states. We reconstructed the transcriptomic and epigenetic zonation of endothelial cells and expanded choroidal stromal heterogeneity by characterizing 11 fibroblast and two pericyte types. Age-associated compositional analysis revealed a significant fractional depletion of melanocytes, PI16+ fibroblasts, and venule endothelial cells with age, alongside a modest relative loss of central RPE and a corresponding increase in far-peripheral RPE. Cell-type-specific aging transcriptomics uncovered shared pathways related to inflammatory responses alongside distinct cell-type-specific signatures. Notably, significant age-associated epigenetic changes concentrated in the macula during the transition from early-to-middle adulthood and remained stable into old age, with transcription factors from the AP-1/bZIP family emerging as the dominant enriched motifs. Finally, integrating this atlas with AMD GWAS data provides novel variant-to-gene evidence implicating LIPG and COL4A3 in AMD pathogenesis. Together, this multi-omics atlas serves as both an invaluable community reference and a powerful discovery engine that translates genetic risk signals into localized target cells and candidate mechanisms, laying a foundation for understanding RPE/choroid biology in health and disease.

genomics↗

bioRxiv2026

An Integrated muti-omics cell atlas of the human trabecular meshwork and ciliary body

The trabecular meshwork (TM) and ciliary body (CB) regulate aqueous humor dynamics and intraocular pressure (IOP), and TM/Schlemms canal (SC) dysfunction underlies glaucoma. Here, we present a spatially resolved multi-omics atlas of human TM and CB, integrating snRNA-seq, scRNA-seq, and snATAC-seq from over one million cells and nuclei across 112 donors with Xenium spatial transcriptomics. We identified 9 major cell classes and 21 cell types, revealing heterogeneity, including undercharacterized fibroblast and epithelial subpopulations. Spatial mapping supported TM fibroblast zonation and CB epithelial organization. Regulatory analyses identified cell type-specific programs, including OTX/PAX networks in CB epithelium and SMAD3/TGF-{beta} signaling in fibroblasts. Integration with glaucoma loci showed enrichment of non-coding variants in regulatory elements associated with POAG and PACG. Age- and ancestry-associated remodeling revealed divergent fibroblast aging with increased PIEZO1, suggesting impaired outflow and elevated IOP. Together, this high-resolution atlas links cellular, regulatory, and genetic variation to anterior segment function and glaucoma susceptibility.

genomics↗

bioRxiv2026

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases

The BRAIN Initiative Cell Atlas Network (BICAN) is generating large-scale multimodal datasets to profile cell types in the human, non-human primate, and mouse brain. The diversity of single-cell and spatial transcriptomic and epigenomic assays, combined with varied experimental contexts, multiple data-generating laboratories and distributed infrastructure, poses substantial challenges for data integration and reuse in BICAN. To address this, we implemented a standards framework that enables layered integration of these data into knowledge-ready products for interoperable brain cell atlases. This framework organizes data based on three progressively structured layers. First, we introduced an assay-agnostic modeling layer that unifies the representation of single-cell and spatial omics data using a common set of biological entities and processes assessed by diverse experimental techniques. Second, we implemented harmonized metadata standards that capture key experimental features linked to biospecimen provenance across heterogeneous tissue sources, species, and preparations, supporting integration and validation while minimizing burden on data contributors. Third, we present an extensible representation for data-driven cell type taxonomies that integrates molecular data with annotations, ontology mappings, and evidence. Together, these contributions represent an end-to-end framework that transforms heterogeneous datasets into structured, interoperable resources that support broad community reuse via mapping algorithms, annotation systems, and visualization platforms. This approach links biospecimen provenance with cell-level outputs and embeds these in a standardized taxonomy format, enabling downstream applications such as cross-dataset integration, reference mapping, and knowledge-driven analysis. More broadly, our work demonstrates a generalizable strategy for enabling an efficient data-to-knowledge pipeline in a large-scale consortium setting.

genomics↗

bioRxiv2026

Cell phenotypes in the biomedical literature: a systematic analysis and text mining corpus

The variety of cell phenotypes identified by single-cell technologies is rapidly expanding, yet this knowledge is dispersed across the scientific literature and incompletely represented in structured resources. We present the CellLink corpus, a manually annotated collection of over 22,000 mentions of human and mouse cell populations in recent journal articles, distinguishing specific cell phenotypes, heterogeneous cell populations, and vague cell populations, and linking to Cell Ontology (CL) terms as either exact or related matches, covering nearly half of the terms in the current CL. A systematic analysis reveals lineage-specific patterns in how authors utilize anatomical context, molecular signatures, functional roles, developmental stage, and other attributes in cell naming. We show that fine-tuning transformer-based models on CellLink yields strong performance for named entity recognition, while embedding-based approaches support zero-shot entity linking and distinguishing exact from related matches. We further demonstrate the utility of CellLink to expand and refine the chondrocyte branch of CL.

bioinformatics↗

bioRxiv2025

B cell transcriptomics reveals lasting dysregulation and rapid decline of protective memory after hepatitis C cure

O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/664545v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@d12426org.highwire.dtl.DTLVardef@9c54daorg.highwire.dtl.DTLVardef@186d559org.highwire.dtl.DTLVardef@120f2f3_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG HighlightsO_LIB cells in CHC patients retained dysregulated transcriptional profiles despite successful DAA treatment C_LIO_LIB cell dysregulation is marked by global B cell hyperactivation and antigen-specific atypical MBC expansion C_LIO_LISustained upregulation of TNF- signaling via NF-{kappa}B (TNF-/NF-{kappa}B) is a central driver of persistent B cell dysregulation and chronic inflammation C_LIO_LIViral clearance leads to restoration of IFN responses and IFN-stimulated gene (ISG) signatures in B cells but not TNF-/NF-{kappa}B C_LIO_LIHCV IGHV1-69 bnAb-producing B cells are enriched within the CD86hi IgG MBC subset before treatment C_LIO_LICD86hi IgG MBC subset contracts rapidly post-cure in parallel with the rapid decline of cross-nAb responses and loss of protective immune memory against HCV reinfection C_LI Chronic hepatitis C (CHC) disrupts host humoral immune response by impairing the timely generation of neutralizing antibody (nAb) and durable immune memory. However, the underlying mechanisms and their reversibility after viral clearance remain poorly defined. Here, through integrated single-cell transcriptomics and antibody repertoire characterization, we show that B cells from CHC patients retain transcriptional dysregulation even after successful antiviral therapy. Sustained TNF- signaling emerged as a central driver of chronic B cell hyperactivation, persistent dysregulation and unresolved inflammation following cure. Furthermore, a CD86hi memory B cell subset, responsible for an IGHV1-69-encoded multi-donor class recall nAb response, declined rapidly following viral clearance, compromising immune memory against reinfection. Together, these findings reveal how CHC imprints lasting B cell dysregulation, impairs nAb memory, and sustains inflammation in the B cell compartment, after viral clearance. The insights underscore the need for strategies aimed at restoring B cell homeostasis to achieve durable immune protection.

immunology↗

bioRxiv2025

VO: The Vaccine Ontology

With the widespread use of vaccines in research and clinical settings, there is an urgent need to standardize vaccine representation, integrate information across diverse vaccine types, and support computer-assisted reasoning. Accordingly, we have since 2007 developed the community-based Vaccine Ontology (VO), which aligns with the Basic Formal Ontology and adheres to OBO Foundry principles. VO models ontologically vaccines, vaccine components, vaccine immune responses, vaccine investigation studies and other vaccine-related topics. VO represents more than 10,000 vaccines targeting 289 infectious pathogens and cancers in humans and over 30 nonhuman animal species. VO provides mappings to external resources such as RxNorm, CVX, FDA, and USDA. Various VO use cases exist. VO facilitates vaccine standardization in resources such as the VIOLIN vaccine database, ImmPort, and the Vaccine Adjuvant Compendium (VAC). Semantic queries can be made to query VO. VO has been shown to enhance experimental and clinical vaccine data analysis and vaccine literature mining. Overall, VO standardizes vaccine modeling and representation and greatly supports vaccine AI research in the Semantic Web era.

bioinformatics↗

bioRxiv2025

A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography

Developing high-resolution reference maps of disease-susceptible spatial niches is a critical step to mitigating the profound effects of lung disease. Here, we present an integrated multimodal single-nucleus human lung atlas (snHLA) profiling 746,047 nuclei from 49 mapped lung blocks spanning clinically relevant distal airways, alveoli, and interstitium across 11 healthy adults. Integrating snRNA-seq and SNARE-seq2, which co-assays chromatin accessibility and gene expression from the same nucleus, we resolved 70 molecularly distinct populations and captured 332,846 accessible chromatin regions, nominating new transcriptional regulators of human lung cell diversity. Spatial transcriptomics using MERFISH mapped 25 cell populations across 7 structural neighborhoods and multiplexed immunofluorescence localized cell subtypes and distal airway-defining protein markers, expanding and validating distinct lung structure-specific cell populations. This open access snHLA and companion Cell Type and Marker Gene Dictionary with anatomically aligned nomenclature delivers a foundational resource at an unprecedented resolution to interrogate the origins of lung pathophysiology.

cell biology↗

bioRxiv2025

Benchmarking single cell transcriptome matching methods for incremental growth of reference atlases

BackgroundThe advancement of single cell technologies has driven significant progress in constructing a multiscale, pan-organ Human Reference Atlas for healthy human cells. Many multi-faceted cell atlases for different organs, species, and diseases now exist, though challenges remain in harmonizing cell types and unifying nomenclature among respective cell atlases. Multiple machine learning and artificial intelligence methods, including models pre-trained on large-scale cell atlas datasets, are publicly available for single cell community users to computationally map their cell clusters to the cell atlases. ResultsThis study benchmarks seven computational tools for cell type matching and label transfer - Azimuth, CellTypist, CellHint, FR-Match, scArches, scPred, and singleR - in ten organ systems. Using healthy lung as an exemplary organ, when matching the well-annotated cell types in two atlases - the Human Lung Cell Atlas (HLCA) and the LungMAP Single-Cell Reference (CellRef), variations in the matching accuracy were observed, especially in rare cell types, underlining the need for a consensus strategy using a selective set of computational methods. In the meta-analysis, the benchmarked methods were used to incrementally integrate 61 cell types from HLCA and 48 from CellRef, resulting in a cell meta-atlas of 41 matched, 20 HLCA-, and 7 CellRef-specific cell types. Similar approach revealed 25 matched cell types existed in two independent kidney atlases. Generalizability of the benchmarking performances were further demonstrated in a variety of organ systems. ConclusionThis study reveals complementing strengths of the benchmarked methods and presents a framework for incremental growth of cell types in cell atlases.

bioinformatics↗

bioRxiv2024

Discovery of cell type classification marker genes from single cell RNA sequencing data using NS-Forest

The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.

bioinformatics↗

bioRxiv2023

Selective loss of specific subsets of hair cell and spiral ganglion cell types in an outbred mouse model of age-related hearing loss

Age-related hearing loss (ARHL) is the most common cause of hearing loss and one of the most prevalent conditions affecting the elderly worldwide. Despite evidence from our lab and others about its polygenic nature, little is known about the specific genes, cell types and pathways involved in ARHL, impeding the development of therapeutic interventions. In this manuscript, we describe, for the first time, the complete cell-type specific transcriptome of the aging mouse cochlea using snRNA-seq in an outbred mouse model in relation to auditory threshold variation. Cochlear cell types were identified using unsupervised clustering and annotated via a three-tiered approach - first by linking to expression of known marker genes, then using the NS-Forest algorithm to select minimum cluster-specific marker genes and reduce dimensional feature space for statistical comparison of our clusters with existing publicly-available data sets on the gEAR website (https://umgear.org/), and finally, by validating and refining the annotations using Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH) and the cluster-specific marker genes as probes. We report on 60 unique cell-types expanding the number of defined cochlear cell types by more than two times. Importantly, we show significant specific cell type increases and decreases associated with loss of hearing acuity implicating specific subsets of hair cell subtypes, ganglion cell subtypes, and cell subtypes withing the stria vascularis in this model of ARHL. These results provide a view into the cellular and molecular mechanisms responsible for age-related hearing loss and pathways for therapeutic targeting.

genomics↗

bioRxiv2023

Targets and cross-reactivity of human T cell recognition of Common Cold Coronaviruses

The Coronavirus (CoV) family includes a variety of viruses able to infect humans. Endemic CoVs that can cause common cold belong to the alphaCoV and betaCoV genera, with the betaCoV genus also containing subgenera with zoonotic and pandemic concern, including sarbecoCoV (SARS-CoV and SARS-CoV-2) and merbecoCoV (MERS-CoV). It is therefore warranted to explore pan-CoV vaccine concepts, to provide adaptive immune protection against new potential CoV outbreaks, particularly in the context of betaCoV sub lineages. To explore the feasibility of eliciting CD4+ T cell responses widely cross-recognizing different CoVs, we utilized samples collected pre-pandemic to systematically analyze T cell reactivity against representative alpha (NL63) and beta (OC43) common cold CoVs (CCC). Similar to previous findings on SARS-CoV-2, the S, N, M, and nsp3 antigens were immunodominant for both viruses while nsp2 and nsp12 were immunodominant for NL63 and OC43, respectively. We next performed a comprehensive T cell epitope screen, identifying 78 OC43 and 87 NL63-specific epitopes. For a selected subset of 18 epitopes, we experimentally assessed the T cell capability to cross-recognize sequences from representative viruses belonging to alphaCoV, sarbecoCoV, and beta-non-sarbecoCoV groups. We found general conservation within the alpha and beta groups, with cross-reactivity experimentally detected in 89% of the instances associated with sequence conservation of >67%. However, despite sequence conservation, limited cross-reactivity was observed in the case of sarbecoCoV (50% of instances), indicating that previous CoV exposure to viruses phylogenetically closer to this subgenera is a contributing factor in determining cross-reactivity. Overall, these results provided critical insights in the development of future pan-CoV vaccines.

immunology↗

bioRxiv2022

The BRAIN Initiative Cell Census Data Ecosystem: A User's Guide

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

bioinformatics↗

bioRxiv2022

Defining antigen targets to dissect vaccinia virus (VACV) and Monkeypox virus (MPXV)-specific T cell responses in humans

The current Monkeypox virus (MPXV) outbreak in non-endemic countries is raising concern about the pandemic potential of novel orthopoxviruses. Little is known regarding MPXV immunity in the context of MPXV infection or vaccination with Vaccinia-based vaccines (VACV). As with vaccinia, T cells are likely to provide an important contribution to overall immunity to MPXV. Here we leveraged the epitope information available in Immune Epitope Database (IEDB) on VACV to predict potential MPXV targets recognized by CD4+ and CD8+ T cell responses. We found a high degree of conservation between VACV epitopes and MPXV, and defined T cell immunodominant targets. These analyses enabled the design of peptide pools able to experimentally detect VACV-specific T cell responses and MPXV cross-reactive T cells in a cohort of vaccinated individuals. Our findings will facilitate the monitoring of cellular immunity following MPXV infection and vaccination.

immunology↗

bioRxiv2022

Reference-based cell type matching of spatial transcriptomics data

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly. Spatial transcriptomics provides spatial location and pattern information about cells in tissue sections at single cell resolution. Cell type classification of spatially-resolved cells can also be inferred by matching the spatial transcriptomics data to reference single cell RNA-sequencing (scRNA-seq) data with cell types determined by their gene expression profiles. However, robust cell type matching of the spatial cells is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that while matching results of individual algorithms vary to some degree, they also show agreement to some extent. We present two ensembl meta-analysis strategies to combine the individual matching results and share the consensus matching results in the Cytosplore Viewer (https://viewer.cytosplore.org) for interactive visualization and data exploration. The consensus matching can also guide spot-based spatial data analysis using SSAM, allowing segmentation-free cell type assignment.

bioinformatics↗

bioRxiv2022

Genetic Variation and Disease Severity of Respiratory Syncytial Viruses

Respiratory Syncytial Virus (RSV) disease in newborns ranges from mild symptoms to severe disease requiring hospitalization. RSV is classified into two subtypes (RSVA and RSVB) based on antigenic and genetic differences. The role these genomic variations play in disease severity remains unknown. Genome sequences were obtained using next-generation RNA sequencing on archived frozen nasal swabs of young children (< 8 months-old) infected with RSV in Rochester, NY between 1977-1998. Samples were chosen from both children hospitalized with severe RSV disease (inpatient) and those presenting with mild symptoms (outpatient) during their first cold-season. Both A and B subtypes demonstrated significant differences in the phylogeny and primary-protein structure during this time period. We found a significant association between RSV phylogeny over this time period and disease severity. For both subtypes, the G-protein demonstrated the greatest amino acid substitutions, although the number of amino acid substitutions was higher in the RSVA subtype. We found a significant association between G-protein variation and disease severity for RSVA, but not RSVB. For both subtypes, variation in the M2-2 protein was significantly associated with disease severity. These results suggest that the genetic variability of RSV proteins may contribute to disease severity in humans. ImportanceEach cold-season Respiratory Syncytial Virus (RSV) infects thousands of children in the US. Some will display mild cold symptoms while others develop severe disease, sometimes resulting in lifelong lung problems or fatality. RSV initiates infection and replicates in the nasopharynx. Substitutions in the RSV genome can be found in clinically isolated nasal-swab samples of RSV infected children. Whether these genome variations contribute to severe disease is unknown. Here we found a statistically significant association between RSV phylogeny and disease severity. Furthermore, we found specific RSV proteins (G and M2-2) whose amino acid variation was statistically associated with severe disease, although which protein was associated depended on subtype. Taken together, our results suggest that RSV genotype contributed to disease severity over this time period.

microbiology↗

bioRxiv2021

Identifying common and novel cell types in single-cell RNA-sequencing data using FR-Match

Reference cell type atlases powered by single cell transcriptomic profiling technologies have become available to study cellular diversity at a granular level. We present FR-Match for matching query datasets to reference atlases with robust and accurate performance for identifying common and novel cell types and suboptimally clustered cell types in the query data. FR-Match shows excellent performance for cross-platform, cross-sample type, and cross-tissue region cell type matching.

bioinformatics↗

bioRxiv2021

Brain Data Standards Ontology: A data-driven ontology of transcriptomically defined cell types in the primary motor cortex

Large-scale single-cell omics profiling is revolutionising our understanding of cell types in complex organs like the brain, where it is being used to define a complete catalogue of cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem. How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a solution to recording definitions, classifications, and properties of cell types and provide standard identifiers for annotation, but they currently do not support the data driven cell type definitions and classifications needed for multi-modal single cell omics profiling. Here we describe the construction and application of a semi-automated, data-linked extension to the Cell Ontology that represents cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole brain atlases currently in preparation.

neuroscience↗