Biology subjects

Gomez-Cabrero, D.

Publications and source records attributed to Gomez-Cabrero, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Single-cell analysis suggests coordinated immune-cell redistribution between blood and cerebrospinal fluid in early multiple sclerosis

Despite immune-cell infiltration being a hallmark of multiple sclerosis (MS), the interplay between the periphery and central nervous system immune responses is still incompletely characterized. We performed single-cell transcriptomic and V(D)J sequencing of paired blood and cerebrospinal fluid (CSF) immune cells from treatment-naive relapsing-remitting women with MS and compared them to age- and sex-matched healthy controls. Across major immune lineages, we identified coordinated, compartment-specific immune alterations, with enrichment of activated and memory lymphocyte populations in the CSF and concomitant depletion of related populations in peripheral blood, suggesting their recruitment from blood to CSF. Clonally expanded CD4 memory T-cells, together with activated, expanded IgM-positive B-cells, accumulated predominantly in the CSF of people with MS compared to healthy subjects. Interestingly, tissue-primed cytotoxic populations and CXCR3-associated memory populations were depleted from the CSF of MS, implying their recruitment to the target tissue in early disease. These findings reveal coordinated, compartment-specific immune changes in early MS and provide a systems-level view of immune-cell trafficking between peripheral and central nervous system compartments.

immunology↗

bioRxiv2026

Mapping Disease Transitions from Premalignant, Asymptomatic to Advanced Myeloma through Integrative Epigenomic and Transcriptional Analyses

AbstractMultiple myeloma (MM) evolves from asymptomatic precursor conditions through progressive genetic and epigenetic remodeling, yet the regulatory mechanisms driving the development toward more active stages of the disease remain poorly understood. Here, we integrated bulk-based paired chromatin accessibility and activation, and transcriptomic profiling across disease stages to map regulatory remodeling during myeloma development. We identified a progressive increase in chromatin accessibility; furthermore, this epigenetic reconfiguration is accompanied by a stage-dependent shift from promoter-centered regulation in precursor states toward enhancer-dominated transcriptional control in active MM. Motif enrichment and regulatory network analyses identified both established and previously underappreciated transcription factors (TFs), including members of the IRF, MEF2, and FOX families, associated with disease-stage-specific transcriptional programs. Among these, MEF2D and FOXK2 emerged as candidate regulators of pathways involved in cell survival and chemotaxis. Functional perturbation demonstrated that MEF2D depletion markedly impaired MM cell viability, whereas inhibition of either MEF2D or FOXK2 reduced chemotactic migration. Together, these findings provide a stage-resolved framework of epigenetic and transcriptional remodeling across myeloma development, revealing regulatory programs established in precursor conditions and progressively reinforced during disease evolution, while identifying candidate transcriptional dependencies with potential biological and therapeutic relevance.

cancer biology↗

bioRxiv2026

Co-regulatory changes in Homo sapiens prefrontal cortex shape RNA-chromatin interactions

Noncoding RNAs (ncRNAs) regulate gene expression through RNA-chromatin interactions, yet their role in human brain evolution remains unclear. To map these interactions, we integrated FANTOM6 neurogenic data with a comprehensive atlas of regulatory regions active during early cortical development. This integration effort linked ncRNAs and transcription factors (TFs) to their genomic targets, revealing functional interactions captured by RNA-DNA contact mapping, yet largely missed by chromatin conformation data. Overlaying these interactions with nearly fixed variants in Homo sapiens relative to extinct hominins uncovered widespread rewiring of TF binding, affecting genes implicated in progenitor proliferation and neuronal differentiation. Gene regulatory network reconstruction identified TEAD2 and ONECUT2 as key regulators of intermediate progenitors and migrating neurons. Functional perturbation followed by Cut&Tag and ATAC-seq linked the two TFs to disease- and evolutionarily- relevant pathways. Together, these results show how ncRNA-TF cooperation and selective cis-regulatory rewiring contributed to sapiens-specific features of cortical development.

molecular biology↗

bioRxiv2026

Establishment of the Saudi Bank of Induced Pluripotent Stem Cells (SBiPSCs): A National Platform for iPSC-Based Research and Therapy

BackgroundThe global landscape of induced pluripotent stem cell (iPSC) resources remains heavily skewed toward European, North American, and East Asian populations, leaving the Middle East and North Africa (MENA) region critically underrepresented. This disparity hinders the application of precision medicine in populations with unique genetic backgrounds, particularly those with high rates of consanguinity and distinctive rare disease profiles. To address this gap, we established the Saudi Bank of Induced Pluripotent Stem Cells (SBiPSCs), at King Abdullah International Medical Research Centre (KAIMRC). The bank comprises two major complementary arms: one dedicated to the derivation and biobanking of iPSCs from individuals with rare and common genetic disorders, and a second focused on Human Leukocyte Antigen (HLA)-based iPSC banking to support the development of immunocompatible cell therapies. MethodsSBiPSCs operates within King Abdulaziz Medical City in Jeddah under the Ministry of National Guard for Health Affairs (MNGHA)s ethical and clinical framework. To establish the repository, we implemented a clinic-guided enrolment strategy in which treating physicians, briefed on the banks objectives, recruited patients with confirmed genetic diagnoses. Peripheral blood samples were collected, processed, and cells were reprogrammed using non-integrating episomal plasmids. All derived lines underwent rigorous quality control in accordance with International Society for Stem Cell Research (ISSCR) standards, including assessment of pluripotency markers, genomic integrity, and trilineage differentiation potential. To demonstrate our iPS characterization workflow and translational utility, iPSCs from a Saudi patient with familial Long QT Syndrome (LQTS) and a healthy sibling were differentiated into functional cardiac organoids. Simultaneously, for the HLA-based banking arm, the Saudi Stem Cell Donor Registry (SSCDR) database was leveraged to identify donors predicted to provide maximal coverage for the Saudi population. ResultsTo date, SBiPSCs has successfully generated 37 iPSC lines derived from 19 Saudi patients and healthy donors. All lines exhibit robust expression of pluripotency markers, maintain normal karyotypes, and demonstrate differentiation capacity. To demonstrate our characterization pipeline and translational utility, iPSCs from an LQTS patient and a healthy sibling were generated, validated, and differentiated into beating cardiac organoids that recapitulated the disease phenotype, with microelectrode array analysis confirming prolonged field potential durations mirroring the clinical QT prolongation. Furthermore, the HLA-based banking arm has expanded to include two homozygous iPSC lines, which together provide immunological compatibility for approximately 9% of the Saudi population. ConclusionsSBiPSCs represents the first centralized iPSC repository in the MENA region. The SBiPSCs is well-positioned to accelerate the translation of stem cell research into scalable, immunocompatible cell therapies and precision medicine applications aligned with national and regional healthcare priorities.

genetics↗

bioRxiv2025

Unify: Learning Cellular Evolution with Universal Multimodal Embeddings

Integrating single-cell RNA-sequencing (scRNA-seq) data across species is hindered by evolutionary divergence, technical batch effects, and the reliance on one-to-one orthologs. We present Unify, a transfer learning methodology that learns universal cell embeddings by defining functionally coherent, multi-modal macrogenes. This is achieved by combining RNA expression with embeddings from protein language models and general-purpose language models. Unify transcends species boundaries, enabling cross-species comparisons beyond strict gene-level homology. Unify corrects batch effects while preserving conserved biological signals across vast evolutionary distances and enables more accurate prediction of perturbation responses across species, such as from mouse to human. Applied to species separated by over 700 million years, Unify reconstructs more accurate multi-species cell-type evolutionary trees and uncovers convergent gene programs. Together, these results establish Unify as a powerful method for comparative single-cell genomics and evolutionary biology.

bioinformatics↗

bioRxiv2025

Human growth plates house resting zone sub-populations with features of quiescent stem cells

Incomplete mapping of gene expression within human (epiphyseal) growth plates contributes to the challenges of diagnosing and treating patients with skeletal growth disorders. To address this issue, we applied spatially resolved transcriptomics to rare growth plate biopsies obtained from healthy adolescents. In addition to identifying novel markers of each zone of the human growth plate, spatial profiling revealed that the expression of genes associated with poorly understood growth disorders, including NKX3-2, SGMS2 and WNK4, is restricted to specific human growth plate zones. By elaborating on the low transcriptional activity of resting zone chondrocytes, we found that a subset of these cells exists in a functionally quiescent state in vivo, as determined by their predominantly nuclear mRNA, abundant heterochromatin, and ability to exit the G0 phase under specific conditions - features shared with skeletal stem cells in mouse growth plates. Additionally, we identified distinct and overlapping sub-populations of human resting zone chondrocytes; an exploration of their hierarchy determined that CHRDL2 and/or SFRP5-positive sub-populations are among the least quiescent resting zone cells. In summary, we generated the most comprehensive gene expression characterization of the human growth plate, which revealed novel zone-specific markers, new primary growth disorders, candidate pharmacological targets, and led us to uncover sub-populations of resting zone chondrocytes with features of quiescent stem cells. These results contribute to a better understanding of the cellular and molecular mechanisms governing human height and can facilitate improved diagnosis and treatment strategies of patients with skeletal growth disorders.

molecular biology↗

bioRxiv2025

Single-cell transcriptional profiling reveals a novel RAB13+ endothelial subpopulation and profibrotic mesenchymal cells in the aged human bone marrow

The bone marrow (BM) microenvironment plays a crucial role in regulating hematopoiesis, yet the molecular changes associated with aging in humans remain poorly understood. Using single-cell RNA sequencing, we uncovered transcriptional shifts in BM endothelial cells (EC) and mesenchymal stem cells (MSC) during aging. Aged sinusoidal EC exhibited a prothrombotic phenotype with compromised mitochondrial and vascular function. Additionally, we identified a novel arterial EC subset, emerging in aged individuals, characterized by RAB13 expression and associated with transcriptional regulatory processes. MSC from aged subjects displayed impaired matrix remodeling and epithelial-mesenchymal transition, driven partly by a subpopulation of THY1+ profibrotic cells absent in younger individuals. Finally, immunofluorescent imaging and spatial transcriptomics confirmed the presence of these aging-associated cells in BM samples from aged individuals. In summary, this work provides a comprehensive view of the transcriptional landscape, cellular interactions, and spatial organization of aged EC and MSC, offering novel insights and potential targets that could be exploited for preventing age-associated changes in humans. TeaserAging reshapes the bone marrow with emergence of RAB13+ endothelial cells and profibrotic stromal cells altering tissue function.

molecular biology↗

bioRxiv2024

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

BackgroundGene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comprehensive exploration and interpretation of biological findings. To address this challenge, we developed GeneSetCluster, an R package designed to summarize and integrate GSA results. Over time, we and users as well identified limitations in the original version, such as difficulties in managing redundancies across multiple gene-sets, large computational times, and its lack of accessibility for users without programming expertise. ResultsWe present GeneSetCluster 2.0, a comprehensive upgrade that delivers methodological, computational, interpretative, and user-experience enhancements. Methodologically, GeneSetCluster 2.0 introduces a novel approach to address duplicated gene-sets and implements a seriation-based clustering algorithm that reorders results, aiding pattern identification. Computationally, the package is optimized for parallel processing, significantly reducing execution time. GeneSetCluster 2.0 enhances cluster annotations by associating clusters with relevant tissues and biological processes to improve biological interpretation, particularly for human and mouse data. To broaden accessibility, we have developed a user-friendly web application enabling non-programmers to use it. This version also ensures seamless integration between the R package, catering to users with programming expertise, and the web application for broader audiences. We evaluated the updates in a single-cell RNA public dataset. ConclusionGeneSetCluster 2.0 offers substantial improvements over its predecessor. Furthermore, by bridging the gap between bioinformaticians and clinicians in multidisciplinary teams, GeneSetCluster 2.0 facilitates collaborative research. The R package and web application, along with detailed installation and usage guides, are available on GitHub (https://github.com/TranslationalBioinformaticsUnit/GeneSetCluster2.0), and the web application can be accessed at https://translationalbio.shinyapps.io/genesetcluster/.

bioinformatics↗

bioRxiv2024

Unravelling the molecular activation of the reparative cardiac fibroblasts after myocardial infarction

Activated cardiac fibroblasts (Postn+ CFs) are responsible for the healing of the heart tissue after a myocardial infarction (MI). However, so far little is known about the moment that CFs are activated, and the genes involved in this process. This is especially relevant in the context of CF heterogeneity and their role in the response to the damage. In this context, we have described a subpopulation of activated CFs responsible for the healing scar and for preventing the rupture of the ventricle after the damage: the Reparative Cardiac Fibroblasts (RCFs). Our new data indicate that RCFs directly derived from activated CFs, and this transcriptional shift happens in a close window after damage. Interestingly, our results exhibited two different molecular dynamics that would give rise to this activation and, consequently, the appearance of definitive RCFs. Using bulk RNA-Seq, RNAScope and Spatial Transcriptomics, we anatomically localized some of the genes related to both dynamics in the infarcted heart and highlight the potential role of Aspn as a new marker of this transcriptional transition in mice, pigs and patients.

bioinformatics↗

bioRxiv2024

PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs

Predicting the transcriptional response of chemical perturbations is crucial to understanding gene function and developing drug candidates, promising a streamlined drug development process. Single-cell sequencing has provided an ideal data basis for training machine learning models for this task. Recent advances in deep learning have led to significant improvements in predictions of chemical as well as genetic perturbations at the single cell level. Experiments have shown that different cell types exhibit distinct transcriptional patterns and responses to perturbation. This poses a fundamental problem for predicting transcriptional responses of drugs or cell types outside the training data. Accordingly, existing methods lack cell-type-specific modeling or do not explicitly provide an interpretable mechanism for the gene features. In this study, we introduce a novel approach that employs a network representation of various cell types as an inductive bias, improving prediction performance in scenarios with limited data while acknowledging cellular differences. We applied our framework to four small-scale single-cell perturbation datasets and one large-scale screening experiment, demonstrating that this representation can inherently generalize to previously unseen cell types. Furthermore, our method outperforms the state-of-the-art methods in predicting the post-perturbation response in unobserved cell types.

bioinformatics↗

bioRxiv2024

Spatial Transcriptomics Reveals a Myeloma Cell Architecture with Dysfunctional T-Cell Distribution, Neutrophil Traps, and Inflammatory Signaling

The bone marrow (BM) is a complex tissue where spatial relationships influence cell behavior, signaling, and function. Consequently, understanding the whole dynamics of cellular interactions requires complementary spatial techniques that preserve and map the architecture of cell populations in situ. We successfully conducted spatial transcriptional profiling using Visium Spatial Gene Expression in formalin-fixed paraffin-embedded (FFPE) BM samples obtained from healthy and Multiple Myeloma (MM) mouse models and patients, addressing the technical challenges of applying spatial technology to long bone samples. A custom data-analysis framework that combines spatial with single-cell transcriptomic profiles identified both the BM cellular composition and the existing cell relations. This allowed us to visualize the spatial distribution of transcriptionally heterogeneous MM plasma cells (MM-PC). We spatially delineated transcriptional programs associated with MM, including NETosis and IL-17-driven inflammatory signaling, which were inversely correlated to malignant PC-enriched regions. Furthermore, a gradient of MM-PC density spatially correlated with a shift from effector-to-exhausted T cell phenotypes. The translational relevance of our findings was confirmed using FFPE BM biopsies from MM patients with varying levels of malignant PC infiltration. In summary, we provide the first spatial transcriptomics analysis applied to a mouse and human mineralized bone tissue and illustrate the BM cellular architecture of MM, revealing deregulated mechanisms underlying MM intercellular communication.

cancer biology↗

bioRxiv2024

SpatialDiffusion: Predicting Spatial Transcriptomics with DenoisingDiffusion Probabilistic Models

Spatial Transcriptomics (ST) allows deep characterization of the 2D organization of expression data within tissue slices. The ST technology provides a tissue contextualization of deep single-cell profiles. Recently, numerous computational and machine learning methods have addressed challenges such as data quality, augmentation, annotation, and the development of integrative platforms for data analysis. In contrast, here we ask whether unseen spatial transcriptomics data can be predicted and if we can interpolate novel transcriptomic slices. To this end, we adopt a denoising diffusion probabilistic-based model (DDPM) to demonstrate the learning of generative ST models for several tissues. Furthermore, our generative diffusion model interpolates (predicts) unseen slices located "between" the collected finite number of ST slices. This methodology set the stage for learning predictive deep 3D models of tissues from a finite number of spatial transcriptomics slices, thus harboring the advent of AI-augmented spatial transcriptomics.

bioinformatics↗

bioRxiv2024

Transcriptional Characterization of the Stromal and Endothelial Bone Marrow Microenvironment during Progression from MGUS to Multiple Myeloma

The role of the non-immune bone marrow microenvironment (BME) in the transition from monoclonal gammopathy of undetermined significance (MGUS) into clinically active multiple myeloma (MM) remains incompletely defined. To address this, we transcriptionally profiled endothelial cells (EC), mesenchymal stem cells (MSC) and MM cells at single-cell resolution from two genetically engineered mouse models (BIc{gamma}1 and MIc{gamma}1) that recapitulate MGUS to MM progression. Our analysis revealed distinct transcriptional trajectories in EC and MSC, uncovering stage-specific BME-PC interactions shaping disease progression. EC acquired a stress phenotype during MGUS transitioning to angiogenesis in MM, while MSC exhibited early impaired differentiation capacity during MGUS that persisted in MM. Notably, an interferon (IFN)-associated MM signature was detected in EC and MSC from the BIc{gamma}1 model but was absent in the more aggressive MIc{gamma}1 model. Treatment with bortezomib, lenalidomide, and dexamethasone remodeled the BME by suppressing MM-IFN signaling, promoting an adaptive response in EC, and restoring osteogenic potential in MSC-- shifting the niche toward a less tumor-permissive state. Importantly, the MM-IFN signature was validated in patients across the MGUS-to-MM spectrum, supporting the translational relevance of our findings. Together, these data define novel dynamic and targetable alterations in the non-immune BME during myeloma progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/589777v5_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1a0ef62org.highwire.dtl.DTLVardef@c44014org.highwire.dtl.DTLVardef@4b1df6org.highwire.dtl.DTLVardef@19c29a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Joint Learning of Node Semantics and Graph Topology using a Transformer in the sparse network regime

The human interactome is a valuable tool for unraveling disease mechanisms, advancing precision medicine, facilitating drug discovery, and identifying biomarkers. Yet, current interactomes are incomplete, in part due to limited experimental coverage. Therefore, augmenting the human interactome by predicting missing links in the Protein-Protein interaction network (PPI), is a core challenge for precision medicine. This study proposes an end-to-end trainable transformer-based neural network for enhanced aggregation of Gene Ontology (GO) terms features. We augment the models predictive capabilities by incorporating semantic anc2vec features, complementing the structural node2vec embeddings specifically designed for sparse PPIs. As a result, by integrating semantic and graph features, we demonstrate superior performance in link prediction.

bioinformatics↗

bioRxiv2023

Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations

With the emergence of single-cell foundation models, an important question arises: how do these models perform when trained on datasets having an imbalance in cell type distribution due to rare cell types or biased sampling? We benchmark three foundation models, scGPT, scBERT, and Geneformer, using skewed single-cell cell-type distribution for cell-type annotation. While all models had reduced performance when challenged with rare cell types, scGPT and scBERT, performed better than Geneformer. Notably, in contrast to scGPT and scBERT, Geneformer uses ordinal positions of the tokenized genes rather than actual raw gene expression values. To mitigate the effect of a skewed distribution, we find that random oversampling, but not random undersampling, improved the performance for all three foundation models. Finally, scGPT, using FlashAttention, has the fastest computational speed, whereas scBERT is more memory-efficient. We conclude that tokenization and data representation are essential areas of research, and new strategies are needed to mitigate the effects of imbalanced learning in single-cell foundation models. Code and data for reproducibility are available at https://github.com/SabbaghCodes/ImbalancedLearningForSingleCellFoundationModels.

bioinformatics↗

bioRxiv2023

Uncovering the cis-regulatory program of early human B-cell commitment and its implications in the pathogenesis of B-cell acute lymphoblastic leukemia

Dysregulation of the early stages of B-cell lymphopoiesis, orchestrating the development of cellular immunity, may induce malignant transformations. Therefore, it is essential to characterize the gene regulatory network (GRN) driving B-cell lymphopoiesis in healthy individuals to uncover malignancy mechanisms. To this end, we generated a dataset that included paired human data for chromatin accessibility and gene expression in eight B-cell precursor stages, providing the first deep characterization of early B-cell lymphopoiesis, including the identification of regulatory elements and the reconstruction of the GRN. Using this data, we recapitulated well-known regulatory elements and revealed new regulons, such as ELK3, enriched in pro-B cells with a putative role in cell cycle progression. Moreover, a single-cell multi-omics analysis validated and enhanced the resolution of the regulatory landscape recovered by bulk data, revealing MYBL2 and ZNF367 as specific regulons of cycling cell states, and CEBPA associated with lymphoid multipotent progenitors (LMPPs). Importantly, this dataset enabled us to uncover B-cell acute lymphoblastic leukemia (B-ALL) triggers. We identified different cellular origins of malignant transformation depending on the B-ALL subtype, including the association of the ETV6-RUNX1 with pro-B cells and the increased expression of ELK3 in this ALL subtype. Overall, our dataset provides the most comprehensive atlas to date of early human B-cell regulation (B-rex; https://translationalbio.shinyapps.io/brex/), facilitating further understanding of B-cell differentiation in health and disease.

immunology↗

bioRxiv2022

PaliDIS: A tool for fast discovery of novel insertion sequences

The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a bioinformatics pipeline called Palidis that recognises insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying Palidis to 264 human metagenomes identifies 879 unique insertion sequences, with 519 being novel and not previously characterised. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events across bacterial classes. We will continue to apply this tool more widely, building the Insertion Sequence Catalogue, a valuable resource for researchers wishing to query their microbial genomes for insertion sequences. Data SummaryO_LIPalidis is available here: github.com/blue-moon22/palidis C_LIO_LIThe Insertion Sequence Catalogue is available to download here: https://github.com/blue-moon22/ISC C_LIO_LIThe raw reads from the Human Microbiome Project can be retrieved using the download links provided in Supplementary Data 1 C_LIO_LIThe analysis for this paper is available here: github.com/blue-moon22/palidis_paper_analysis C_LIO_LIThe output of Palidis that was run on these reads is available in Supplementary Data 2 C_LI Impact StatementInsertion sequences are a class of transposable element that play an important role in the dissemination of antimicrobial resistance genes. However, it is challenging to completely characterise the transmission dynamics of insertion sequences and their precise contribution to the spread of antimicrobial resistance. The main reasons for this are that it is impossible to identify all insertion sequences based on limited reference databases and that de novo computational methods are ill-equipped to make fast or accurate predictions based on incomplete genomic assemblies. Palidis generates a larger, more comprehensive catalogue of insertion sequences based on a fast algorithm harnessing genomic diversity in mixed microbial communities. This catalogue will enable genomic epidemiologists and researchers to annotate genomes for insertion sequences more extensively and advance knowledge of how insertion sequences contribute to bacterial evolution in general and antimicrobial resistance spread across microbial lineages in particular. This will be useful for genomic surveillance, and for development of microbiome engineering strategies targeting inactivation or removal of important transposable elements carrying antimicrobial resistance genes.

genomics↗

bioRxiv2022

An Atlas of Cells in the Human Tonsil

Palatine tonsils are secondary lymphoid organs representing the first line of immunological defense against inhaled or ingested pathogens. Here, we present a comprehensive census of cell types forming the human tonsil by applying single-cell transcriptome, epigenome, proteome and adaptive immune repertoire sequencing as well as spatial transcriptomics, resulting in an atlas of >357,000 cells. We provide a glossary of 121 annotated cell types and states, and disentangle gene regulatory mechanisms that drive cells through specialized lineage trajectories. Exemplarily, we stratify multiple tonsil-resident myeloid slancyte subtypes, establish a distant BCL6 superenhancer as locally active in both follicle-associated T and B cells, and describe SIX5 as a potentially novel transcriptional regulator of plasma cell maturation. Further, our atlas is a reference map to understand alterations observed in disease. Here, we discover immune-phenotype plasticity in tumoral cells and microenvironment shifts of mantle cell lymphomas (MCL). To facilitate such reference-based analysis, we develop HCATonsilData and SLOcatoR, a computational framework that provides programmatic and modular access to our dataset; and allows the straightforward annotation of future single-cell profiles from secondary lymphoid organs.

immunology↗