Reference Atlas Research Articles

Developmental mouse brain common coordinate framework

3D brain atlases are key resources to understand the brain’s spatial organization and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of developing mouse brain 3D reference atlases hinders advancements in understanding brain development. Here, we present a 3D developmental common coordinate framework (DevCCF) spanning embryonic day (E)11.5, E13.5, E15.5, E18.5, and postnatal day (P)4, P14, and P56, featuring undistorted morphologically averaged atlas templates created from magnetic resonance imaging and co-registered high-resolution light sheet fluorescence microscopy templates. The DevCCF with 3D anatomical segmentations can be downloaded or explored via an interactive 3D web-visualizer. As a use case, we utilize the DevCCF to unveil GABAergic neuron emergence in embryonic brains. Moreover, we map the Allen CCFv3 and spatial transcriptome cell-type data to our stereotaxic P56 atlas. In summary, the DevCCF is an openly accessible resource for multi-study data integration to advance our understanding of brain development.

Integrated multimodal cell atlas of Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia in older adults. Although AD progression is characterized by stereotyped accumulation of proteinopathies, the affected cellular populations remain understudied. Here we use multiomics, spatial genomics and reference atlases from the BRAIN Initiative to study middle temporal gyrus cell types in 84 donors with varying AD pathologies. This cohort includes 33 male donors and 51 female donors, with an average age at time of death of 88 years. We used quantitative neuropathology to place donors along a disease pseudoprogression score. Pseudoprogression analysis revealed two disease phases: an early phase with a slow increase in pathology, presence of inflammatory microglia, reactive astrocytes, loss of somatostatin+ inhibitory neurons, and a remyelination response by oligodendrocyte precursor cells; and a later phase with exponential increase in pathology, loss of excitatory neurons and Pvalb+ and Vip+ inhibitory neuron subtypes. These findings were replicated in other major AD studies.

Comparison of basal ganglia regions across murine brain atlases using metadata models and the Waxholm Space

The murine basal ganglia regions are targets for research into complex brain functions such as motor control and habit formation. However, there are several ways to name and annotate these regions, posing challenges for interpretation and comparison of data across studies. Here, we give an overview of basal ganglia terms and boundaries in the literature and reference atlases, and describe the criteria used for annotating these regions in the Waxholm Space rat brain atlas. We go on to compare basal ganglia annotations in stereotaxic rat brain atlases and the Allen Mouse brain Common Coordinate Framework to those in the Waxholm Space rat brain atlas. We demonstrate and describe considerable differences in the terms and boundaries of most basal ganglia regions across atlases and their versions. We also register information about atlases and regions in the openMINDS metadata framework, facilitating integration of data in neuroscience databases. The comparisons of terms and boundaries across rat and mouse atlases support analysis and interpretation of existing and new data from the basal ganglia.

Neural network architecture of a mammalian brain

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Toxoplasma gondii Infection of Alzheimer's Disease Mice Reduces Brain Amyloid Density Globally and Regionally.

Toxoplasma gondii infection of Alzheimer's disease model mice decreases amyloid β plaques. We aimed to determine if there is a brain regional difference in amyloid β reduction in the brains of T. gondii-infected compared to control mice. Three-month-old 5xFAD (AD model) mice were injected with T. gondii or with phosphate-buffered saline as a control. Intact brains were harvested at 6 weeks postinfection, optically cleared using iDISCO+, and brain-wide amyloid burden was visualized using volumetric light-sheet imaging. Amyloid signal was quantified across each brain and computationally mapped to the Allen Institute Brain Reference Atlas to determine amyloid density in each region. A brain-wide analysis of amyloid in control and T. gondii-infected 5xFAD mice revealed that T. gondii infection decreased amyloid burden in the brain globally as well as in the cortex and hippocampus, and many daughter regions. Daughter regions that showed reduced amyloid burden included the prelimbic cortex, visual cortex, and retrosplenial cortex. The olfactory tubercle, a region known to have increased monocytes following T. gondii infection, also showed reduced amyloid after infection. T. gondii infection of AD mice reduces amyloid burden in a brain region-specific manner that overlaps with known regions of T. gondii infection and peripheral immune cell infiltration.

Small data methods in omics: the power of one.

Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning to summarize a latent space across 147 local (e.g., fiber length, solidity) and global (e.g., fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Furthermore, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive versus outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.

ScType enables fast and accurate cell type identification from spatial transcriptomics data.

The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation. scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial_tutorial.md, respectively.

Publication, funding, and experimental data in support of Human Reference Atlas construction and usage

Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.

Visualisation of gene expression within the context of tissues using an X-ray computed tomography-based multimodal approach

The development of an organism is orchestrated by the spatial and temporal expression of genes. Accurate visualisation of gene expression patterns in the context of the surrounding tissues offers a glimpse into the mechanisms that drive morphogenesis. We developed correlative light-sheet fluorescence microscopy and X-ray computed tomography approach to map gene expression patterns to the whole organism`s 3D anatomy. We show that this multimodal approach is applicable to gene expression visualized by protein-specific antibodies and fluorescence RNA in situ hybridisation offering a detailed understanding of individual phenotypic variations in model organisms. Furthermore, the approach offers a unique possibility to identify tissues together with their 3D cellular and molecular composition in anatomically less-defined in vitro models, such as organoids. We anticipate that the visual and quantitative insights into the 3D distribution of gene expression within tissue architecture, by multimodal approach developed here, will be equally valuable for reference atlases of model organisms development, as well as for comprehensive screens, and morphogenesis studies of in vitro models.

Scholarly publications and data set evidence for the Human Reference Atlas

Abstract Experts from 17 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the human body at single cell resolution. To bridge across scales—from the meter size human body to the micrometer size single-cell level—organ experts are constructing anatomical structures, cell types plus biomarkers (ASCT+B) tables, and associated spatial reference objects. The 3rd HRA (v1.2) release features 26 organ-specific ASCT+B tables that cite 456 scholarly papers and are linked to 61 spatial reference objects and Organ Mapping Antibody Panels (OMAPs); it is authored by more than 120 experts. This paper presents the first analyses and visualizations showcasing what data and scholarly evidence exist for which organs and how experts relate to the organs covered in the HRA. To identify potential HRA authors and reviewers, we queried the Web of Science database for authors who work on the 33 organs targeted for the next HRA release (v1.3). To provide scientific evidence for the HRA, we identified 620 high-quality, single-cell experimental data sets for 58 organs published in 561 unique papers. The results presented are critical for understanding and communicating the quality of the HRA, planning for future tissue data collection, and inviting leading experts to contribute to the evolving atlas.

Multi-organ single-cell RNA sequencing in mice reveals early hyperglycemia responses that converge on fibroblast dysregulation.

Diabetes causes a range of complications that can affect multiple organs. Hyperglycemia is an important driver of diabetes-associated complications, mediated by biological processes such as dysfunction of endothelial cells, fibrosis, and alterations in leukocyte number and function. Here, we dissected the transcriptional response of key cell types to hyperglycemia across multiple tissues using single-cell RNA sequencing (scRNA-seq) and identified conserved, as well as organ-specific, changes associated with diabetes complications. By studying an early time point of diabetes, we focus on biological processes involved in the initiation of the disease, before the later organ-specific manifestations had supervened. We used a mouse model of type 1 diabetes and performed scRNA-seq on cells isolated from the heart, kidney, liver, and spleen of streptozotocin-treated and control male mice after 8 weeks and assessed differences in cell abundance, gene expression, pathway activation, and cell signaling across organs and within organs. In response to hyperglycemia, endothelial cells, macrophages, and monocytes displayed organ-specific transcriptional responses, whereas fibroblasts showed similar responses across organs, exhibiting altered metabolic gene expression and increased myeloid-like fibroblasts. Furthermore, we found evidence of endothelial dysfunction in the kidney, and of endothelial-to-mesenchymal transition in streptozotocin-treated mouse organs. In summary, our study represents the first single-cell and multi-organ analysis of early dysfunction in type 1 diabetes-associated hyperglycemia, and our large-scale dataset (comprising 67 611 cells) will serve as a starting point, reference atlas, and resource for further investigating the events leading to early diabetic disease.

Polytomous key for selecting plants according to decorative, phenological, phenotypic traits when landscaping cities in the south of Russia

The classic selection of plants for landscaping the urban environment is a rather labor-intensive process involving the use of a large number of reference materials, atlases and plant catalogs. It is necessary to consider various characteristics of plants, taking into account their decorativeness, mutual compatibility, survival in the natural conditions of each region, etc. The purpose of the research was developing a polytomous key and creating it using a modern computer program to simplify the selection of plants according to decorative, phenotypic and phenological characteristics for landscaping cities in the southern Russia. The method of compiling a polytomous key was used as a way to identify plants by several leading characteristics, when the original groups were simultaneously divided into several smaller ones. The life forms, size, habitus of plants, characteristics of the trunk and crown, characteristics of flowering and fruiting, as well as environmental requirements were taken into account. On the basis of the research results the assortment of ornamental plants in large nurseries of the Krasnodar Territory and the Republic of Adygea, a list of plant names was compiled and their characteristics were encoded using a polytomous key. The key consisted of 8 sections, thanks to which it was possible to give the most complete description of the plant according to 36 parameters. The resulting database received state registration certificate No. 2023621905. The base and operation of the polytomous key are like a computer program. The functioning of the program has been successfully tested. The created program greatly simplifies the work of choosing an assortment of plants for landscaping, reducing it to a few minutes.

Abstract IA14: Forecasting pancreatic carcinogenesis through spatial multi-omics

Abstract Combining genomics with mathematical modeling provides a forecast system that can yield computational predictions to anticipate cancer progression and therapeutic response. High-throughput profiling technologies can indicate the molecular and cellular pathways of malignancies, but not the effect of targeting those pathways with therapy. Precision interception requires relating therapies to the cellular phenotypes underlying pancreatic carcinogenesis. This talk presents a hybrid computational and experimental strategy to uncover interactions between neoplastic cells and the microenvironment during pancreatic carcinogenesis. As pancreatic cancer develops, it forms a complex microenvironment of multiple interacting cells. The microenvironment of advanced pancreatic cancer includes a heterogeneous and dense population of cells, such as macrophages and fibroblasts, that are associated with immunosuppression. New single-cell and spatial molecular profiling technologies enable unprecedented characterization of the cellular and molecular composition of the microenvironment. These technologies provide the potential to identify candidate therapeutics to intercept immunosuppression in pancreatic cancer. State-of-the-art mathematical approaches in computational biology are essential to uncover mechanistic insights for high-throughput data for these precision interception strategies. We have recently applied new machine learning techniques to integrate imaging and spatial transcriptomics to enable comprehensive characterization of the pancreatic precancer microenvironment. Developing targets for interception requires determining which of these cellular and molecular pathways drive subsequent development of cancer. Relating the transcriptional programs in premalignant lesions to carcinogenesis requires mapping their temporal changes across disease stages. To integrate the spatial molecular data from premalignancies and single-cell RNA-seq reference atlases, we developed a transfer learning approach. This integrated analysis identified a switch between fibroblast-induced inflammatory signaling and cellular proliferation in pancreatic cancer cells. We confirmed these transitions also occur in pancreatic cancer patient derived organoids co cultured with fibroblasts using transfer learning from the reference scRNA-seq atlases of pancreatic tumors. This approach demonstrates the potential of computational analysis of multi-omics data in human tissue with organoid models for bidirectional bench to bedside research. Citation Format: Elana J. Fertig. Forecasting pancreatic carcinogenesis through spatial multi-omics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr IA14.

Ethnic Genesis of Ukrainians in School Atlases of Ukrainian Studies and History in the Context of moscow's Genocidal War Against Ukraine

The article presents the results of the analysis of the content of 8 educational atlases on Ukrainian studies and history of Ukraine for secondary schools, published in 2003–2021. The author singled out and evaluated the knowledge presented in these atlases about the formation (genesis) of the Ukrainian ethnos in the context of Moscow's genocidal war against Ukraine. Atlases are compared with each other in terms of the amount and content of knowledge about the ethnic genesis of Ukrainians. The author compared the aggregate knowledge about the education of Ukrainians available in the atlases with the corresponding thematic content of the curriculum «Ukrainian Studies» for students in grades 5–11 (2020), as well as a number of scientific reference atlases of the history of Ukraine, published in 2010–2018. The article formulates proposals aimed at improving the coverage of the ethnic genesis of Ukrainians in school atlases of Ukrainian studies and history of Ukraine. The article is useful for Ukrainian scholars, historians, geographers who are concerned with improving the educational and methodological support of school courses in Ukrainian studies and history of Ukraine. The author believes that the reviewed thematic content of the program «Ukrainian Studies...», school atlases on Ukrainian studies and on the history of Ukraine needs to be deepened, modernized and concretized. The atlases should contain a clear answer to the question of where, when and as a result of what the ethnic community of Ukrainians was formed. A Ukrainian student should see in the atlas not only the Ukrainian ethnic territory that had already been formed, for example, at the beginning of the 16th or 17th centuries, but also the territory of the formation of the Ukrainian ethnic population, the Ukrainian language, Ukrainian culture, and the Ukrainian ethnos during the relevant historical time. In the same way, knowledge about the ethnic genesis of the Crimeans (Crimean Tatars) should be presented. In the conditions of the genocidal war, Ukrainians' awareness of their origins, the assimilation of scientifically based knowledge about the genesis of the Ukrainian ethnic community by school students have acquired strategic importance for the survival of Ukrainian society, for its ability to solidarize, consolidate, and mobilize in order to protect and counteract the genocidal aspirations of the ruscists.

BUILDING A HUMAN REFERENCE ATLAS

Abstract The Human Reference Atlas (HRA, https://humanatlas.io) is a comprehensive, high-resolution, three-dimensional atlas of all the cells in the healthy human body. The HRA provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions linked to existing ontologies. In this talk, we will present a high-level overview of the major components of the HRA--including 67 anatomically correct 3D Reference Objects for 29 organs and 31 Anatomical Structure, Cell Types, and Biomarker (ASCT+B) Tables--and the tools to explore, use, author, and review the HRA--including the Registration User Interface, the Exploration User Interface, the ASCT+B Reporter, and the HRA Organ Gallery in virtual reality. We welcome experts and practitioners to join the monthly WG meetings (sign up at https://iu.co1.qualtrics.com/jfe/form/SV_bpaBhIr8XfdiNRH), to explore and contribute to this effort, and to provide feedback on the evolving HRA from diverse perspectives.

Precise Single-Cell Transcriptomic Mapping of Leukemia Cell States Reveals Unconventional Lineage Priming in Acute Myeloid Leukemia

Initial disease classification within acute leukemia relies on identifying morphological and immunophenotypic features of hematopoietic differentiation retained by leukemic blasts. While existing approaches can classify leukemic cells into broad lineages, they lack precision in discerning between specific cell states, particularly at the level of immature blasts. Single-cell (sc) RNA-sequencing (RNA-seq) provides thousands of new markers to enable precise determination of leukemia cell state and provides an opportunity to refine our classification of acute leukemia. To extend our ability to identify leukemia cell states by scRNA-seq, we developed a comprehensive reference map of human bone marrow hematopoiesis. Specifically, we integrated three unsorted bone marrow datasets and three CD34+ hematopoietic stem and progenitor (HSPC) purified datasets to ensure balanced representation of early HSPCs and terminally differentiated immune cells. This culminated in a curated atlas of 263,159 high-quality sc transcriptomes, representing 55 cellular states spanning 45 healthy donors (Fig 1A). We validated our cell state annotations to ensure concordance between transcriptional states and functional populations through reference mapping of bulk and sc transcriptomes from purified HSPC subsets. For example, mapping of scRNA-seq profiles from Lin-CD34+38-45RA-90+49f+ long-term (LT) HSCs revealed 91% concordance between transcriptional HSC and functional LT-HSC. We next performed scRNA-seq profiling on 12 AML patient samples harbouring either myelodysplasia-related genetic features or classical alterations involving NPM1 or KMT2A. By mapping leukemia cells onto our reference atlas, we identified pronounced involvement of early erythroid-like blasts in a subset of AML samples. To further investigate unconventional lineage priming in AML, we processed and mapped scRNA-seq profiles from eleven studies comprising 166 additional AML samples and 9 mixed phenotype acute leukemia (MPAL) samples (Fig 1B). After quality control and exclusion of mature lymphoid cells, we performed composition analysis utilizing 600,570 leukemia cells mapped to 38 cell states to identify patterns of variation among leukemia cell states. Immature leukemia cells map to a range of cellular states spanning HSC/MPP-like, LMPP-like, MLP-like, GMP-like, MEP-like, and EarlyEry-like, among others. Notably, we identified a subset of AMLs with high MLP-like enrichment that co-cluster with MPALs as well as a subset with high EarlyEry-like involvement that co-cluster with acute erythroid leukemias (AEL), thus highlighting the biological continuum between these diseases. We next identified marker genes from each leukemia cell state and trained sparse regression models to estimate their relative abundance within bulk RNA-seq cohorts. Through bulk analysis, we confirmed that MPAL is highly enriched for MLP-like cells compared to AML (p=2.5e-12) and that M6 AELs are highly enriched for EarlyEry-like cells compared to M0-M5 AMLs (p=0.00067). Among 864 AML patients, we found that high MLP-like abundance was associated with NUP98-NSD1 fusions (p=0.0011) and RUNX1 mutations (p=8.0e-10) while high EarlyEry-like abundance was associated with Complex Cytogenetics (p=8e-15) and TP53 mutations (p=2.3e-25). Cellular states of immature leukemic cells captured inter-patient heterogeneity within 136 AEL samples, wherein high EarlyEry-like abundance was associated with TP53 mutations (p=3.6e-9) and Poor cytogenetic risk (p=1.1e-9) while high GMP-like abundance was associated with KMT2A alterations (p=0.00046) and Good/Intermediate risk (p=0.00032). Finally, through integration of sc transcriptomics and targeted DNA profiling, we found examples of genetic subclones impacting the cellular hierarchies of individual patients through induction of early differentiation blocks, skewing of lineage output from myeloid to erythroid, or potentiation of transcriptional self-renewal programs within mature myeloid cells. Together, our high-resolution mapping approach highlights the cellular state continuum between acute leukemias and enumerates the impact of genetic drivers on leukemia cell hierarchies. Precise definitions of cellular states, coupled with the presence or absence of genetic alterations, may help to refine future disease classification, prognostication, and therapy development.

IInformation-reference system on human brain development

BACKGROUND: Available information on the intrauterine maturation of the human brain is fragmentary; thus, the systematization of these data in the form of an information-reference system is needed. A modern solution would be the creation of a multimodal digital atlas, which would combine images of the developing brain at the macromorphological, tissue, and cellular levels.
 AIM: This study aimed to create a prototype of an informational reference system on human brain development, incorporating a digital multimodal atlas with the ability to view specific brain regions.
 METHODS: The creation of a prototype informational reference system on the prenatal morphogenesis of the human brain involved the following stages: researching the subject area, developing an informational model, defining automation tasks and functionality of the information system, selecting hardware and software tools, testing, and analyzing the results.
 RESULTS: A prototype of the informational reference system “Human Brain Development Atlas” was developed, consisting of three main blocks for each ontogenetic stage: (1) description of the brain development stage, which includes a macroscopic description of the brain structure, an overview of key morphogenetic events, and galleries with hematoxylin and eosin-, Nissl-, and Mallory-stained sections; reference atlases, which contains annotated maps of brain sections at different stages of prenatal ontogenesis; and 3) immunohistochemical atlases, which provides data on the developmental translational profile of brain cells. Currently, some materials are already available on the project website: https://brainmorphology.science/ru/
 CONCLUSIONS: Modern information technologies can be used for data collection and processing on the prenatal morphogenesis of the human brain. The creation of an informational reference system on the prenatal morphogenesis of the human brain can contribute to the development of new methods for early diagnosis and treatment of various nervous system disorders.
 The data presented in this article were previously published in English in “Life” (doi: 10.3390/life13051182) and are published in “Morphology” in Russian with the consent of the authors and copyright holders and in accordance with the terms of the CC BY license of the primary article.