Biology subjects

Hernaez, M.

Publications and source records attributed to Hernaez, M..

At least 19 recordsLinked to original sources

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

Rational engineering of sdAb-based CAR T cells targeting BCMA enhances antitumor efficacy and persistence in Multiple Myeloma

BCMA-directed CAR T therapies have transformed the treatment of relapsed/refractory multiple myeloma (MM), yet durable responses remain limited by insufficient persistence and functional exhaustion. The contribution of antigen-binding domain properties to CAR T cell performance is not fully defined. Here, we report the rational discovery and engineering of BCMA-targeting single-domain antibodies (sdAbs) and their incorporation into second-generation CAR T cells. We generated sdAbs recognizing distinct BCMA epitopes with diverse binding kinetics. Comprehensive biophysical and functional characterization identified sdAb5 as a lead candidate with balanced binding kinetics and a non-overlapping epitope relative to clinically approved constructs. sdAb-based CAR T cells exhibited potent antigen-specific cytotoxicity, minimal tonic signaling, and preserved IL-2 production without excessive inflammatory activation. In xenograft models, sdAb5-based CAR T cells induced durable tumor control, enhanced persistence, and improved responses to tumor rechallenge compared with ide-cel and cilta-cel constructs. Notably, this superior functionality correlated with balanced binding kinetics rather than maximal affinity. Single-cell transcriptomic analyses corroborated these findings, revealing a restrained inflammatory activation in sdAb5-based CAR T cells with preservation of transcriptional plasticity. Collectively, these findings support antigen-binding optimization as a key determinant of CAR T-cell durability and identify sdAb5 as a candidate for next-generation BCMA-directed therapies in MM.

bioengineering↗

bioRxiv2026

Rapid and dynamic reprogramming within the tumor microenvironment drives EDA-CAR-T dysfunction and compromised therapeutic efficacy in solid tumors

BackgroundChimeric antigen receptor (CAR)-T cell therapies efficacy in solid tumors remains limited, largely due to the profoundly immunosuppressive tumor microenvironment (TME) which drives CAR-T cells to dysfunction and poor persistence. A comprehensive understanding of the dynamic interplay between CAR-T cells and the TME is therefore critical for the rational design of more effective CAR-T strategies for solid cancers. MethodsHere, we performed single-cell RNA sequencing of tumor samples from immunocompetent mice treated with stroma-targeting EDA-CAR-T cells, profiling CAR-T cell states and TME programs at the peak of antitumor response and during subsequent tumor progression. ResultsOur analysis revealed a marked temporal remodeling of EDA-CAR-T cells within the TME, where early antitumor efficacy is associated with concurrent expansion of cytotoxic effector CD8 CAR-T cells and activation of memory CD4 CAR-T subsets. Moreover, EDA-CAR-T cells effectively engaged the myeloid compartment, resulting in strengthened communication networks involving T cell activation. However, by tumor progression, EDA-CAR-T cells suffered a widespread transcriptional reprogramming towards dysfunction, characterized by loss of effector programs alongside induction of exhaustion and immunoregulatory pathways within the TME, including PD-L1/PD-L2 and TGF{beta} signaling, which impairs sustained immune responses. Notably, early CAR-T cell activation led to increased susceptibility to TME-mediated immunosuppression, revealing EDA-CAR-T-specific soluble galectin-mediated cell-to-cell interaction networks. ConclusionsTogether, this works offers a high-resolution view of CAR-T cell dynamics within the solid TME, uncovering cellular and molecular mechanisms of rapid functional decline and identifying regulatory pathways within the TME that can be exploited to improve CAR-T cell therapy efficacy in solid tumors. KEY MESSAGES OF THE ARTICLEO_ST_ABSWhat is already known on this topicC_ST_ABSThe determinants of CAR-T cell therapeutic efficacy in solid tumors remain poorly defined, largely due to the complexity of the immunosuppressive tumor microenvironment. In this effort, it is necessary to perform comprehensive and detailed mechanistic studies that capture CAR-T cell dynamics within the solid tumor microenvironment to understand treatment failure. What this study addsWe performed single-cell profiling of stroma-targeting EDA-CAR-T cells, revealing their dynamic reprogramming toward dysfunction within the solid tumor microenvironment. We dissected CAR-T cell states and their cell-to-cell interactions with the tumor microenvironment across response and tumor progression and identified mechanisms linking CAR-T cell functionality and therapeutic failure. How this study might affect research, practice or policyThis study provides comprehensive mechanistic insights from an immunocompetent model that can be leveraged to identify shared determinants of CAR-T cell functionality in solid tumors and potentially guide the rational development of improved CAR-T cell therapies.

genomics↗

bioRxiv2025

NVUAtlas: A Comprehensive Single-Nucleus RNA-Seq Resource for the Human Neurovascular Unit in Alzheimer's Disease

BackgroundNeurovascular unit (NVU) dysfunction is being recognized as one of the earliest contributors to Alzheimers disease (AD) pathogenesis. However, systematic investigation of NVU dysfunction is currently limited by lack of access to molecular level information due to underrepresentation of vascular and mural cells in standard single-nucleus RNA sequencing (snRNA-seq) datasets. Consequently, existing transcriptomic atlases lack the resolution necessary to capture the coordinated intercellular signaling and dysfunction across vascular components of NVU, including endothelial cells and pericytes. MethodsWe constructed the Human NVU Atlas by integrating 11 publicly available snRNA-seq datasets, including vascular-enriched samples, from the human prefrontal cortex. This comprehensive dataset aggregates over 4.2 million nuclei from 748 donors, including AD patients and age-matched controls. We utilized a unified probabilistic pipeline based on deep generative models (SCVI) to perform batch-aware integration and employed an ensemble of supervised and deep-learning classifiers to rigorously re-annotate cell types. Differential expression and ligand-receptor interaction analyses were subsequently performed to identify cell-type-specific disruptions in males versus females. ResultsThe atlas successfully curated vascular populations from 11 studies to assemble the largest publically available NVU cohort of endothelial cells (2.8%) and pericytes (1.9%) alongside astrocytes and neurons. Differential expression analysis revealed that while neurons predominantly exhibited gene downregulation in AD, vascular cells displayed a pattern of transcriptional hyperactivity with significant gene upregulation. We also identified pronounced sex-specific vulnerabilities; females exhibited distinct inflammatory signatures and downregulation of basement membrane collagen genes (e.g., COL4A1, COL4A2) in pericytes, whereas these changes were not observed in males. Moreover, cell-cell interaction analysis revealed a widespread loss of collagen-integrin signaling between pericytes and neurons, suggesting the involvement of extracellular matrix disruptions in NVU dysfunction observed in AD. ConclusionThe Human NVU Atlas provides a high-resolution, integrated transcriptomic framework for dissecting the cellular heterogeneity of the neurovascular unit. By uncovering sex-specific vascular mechanisms and disrupted intercellular communication, this resource highlights the critical role of vascular cells in AD progression and serves as a foundational reference for investigating cerebrovascular contribution to AD.

neuroscience↗

bioRxiv2025

An open CAR-T single-cell atlas to enable in-depth characterization and rational engineering of CAR-T products

BRIEF ABSTRACTWe built a CAR-T cell functional atlas from over one million cells across 13 studies, integrating data from patients and healthy donors. The atlas captures 11 phenotypes, links infusion product composition with clinical response, and reveals sex- and age-dependent effects, metabolic signatures, and rare ICANS-associated cells. This open-access resource provides a foundation to understand CAR-T cell function and guide the design of next-generation therapies.

bioinformatics↗

bioRxiv2025

Single-Cell Multiomics Reveals Regulatory Mechanisms of CAR T Cell Persistence and Dysfunction in Multiple Myeloma

Understanding the mechanisms that drive chimeric antigen receptor (CAR) T cell function and persistence in multiple myeloma (MM) remains a critical challenge for improving therapeutic outcomes. In this study, we applied single-cell multiomics and gene regulatory network (GRN) analysis to characterize the transcriptional dynamics and clonal evolution of BCMA-targeted CAR T cells in longitudinally collected bone marrow (BM) and peripheral blood (PB) samples from MM patients. Our results revealed that CAR T cells infiltrating BM exhibited a more activated and exhausted phenotype compared to their PB counterparts, with key transcriptional regulators driving these changes. Dysregulation in the effector-to-memory transition led to an increased presence of terminally differentiated CAR T cells, correlating with poor persistence. Additionally, we identified a hyperexpanded CAR T clone in the BM of a patient with partial response, marked by elevated IL10 expression. Functional analyses demonstrated that stimulation of endogenous TCR enhanced IL10 production, potentially contributing to impaired CAR T cell proliferation and persistence. These findings uncover critical regulatory mechanisms influencing CAR T cell dynamics, offering new insights into improving CAR T cell persistence and therapeutic efficacy in MM and highlights potential molecular targets for optimizing CAR T cell therapy in patients with MM.

genomics↗

bioRxiv2025

JIND-Multi: Leveraging Multiple Labeled Datasets for Automated Annotation of Single-Cell RNA and ATAC Data

BackgroundThe creation of single-cell atlases is essential for understanding cellular diversity and heterogeneity. However, assembling these atlases is challenging due to batch effects and the need for accurate cell annotation. Current methods for single-cell RNA and ATAC sequencing, while effective for integration, are not optimized for cell annotation. Additionally, many annotation tools rely on external databases or reference scRNA-Seq datasets, which may limit their adaptability to specific study needs, especially for rare cell-types or scATAC-Seq data. ResultsWe introduce JIND-Multi, an extended version of the JIND framework, designed to transfer cell-type labels across multiple annotated datasets. JIND-Multi significantly reduces the proportion of unclassified cells in single-cell RNA sequencing (scRNA-Seq) data while maintaining the accuracy and performance of the original JIND model. Furthermore, JIND-Multi demonstrates robust and precise annotation results in its inaugural application to scATAC-Seq data, proving its versatility and effectiveness across different single-cell sequencing technologies. ConclusionsJIND-Multi represents an improvement in cell annotation, reducing unassigned cells and offering a reliable solution for both scRNA-Seq and scATAC-Seq data. Its ability to handle multiple labeled datasets enhances the precision of annotations, making it a valuable tool for the single-cell research community. JIND-Multi is publicly available at:https://github.com/ML4BM-Lab/JIND-Multi.git.

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↗

bioRxiv2024

Uncovering functional lncRNAs by scRNA-seq with ELATUS

Long non-coding RNAs (lncRNAs) play fundamental roles in cellular processes and pathologies, regulating gene expression at multiple levels. Despite being highly cell type-specific, their study at single-cell (sc) level has been challenging due to their less accurate annotation and low expression compared to protein-coding genes. To identify the important, albeit widely overlooked, specific lncRNAs from scRNA-seq data, here, we develop a computational framework, ELATUS, based on the pseudoaligner Kallisto that enhances the detection of functional lncRNAs previously undetected and exhibits higher concordance with the ATAC-seq profiles in single-cell multiome data. Importantly, we then independently confirmed the expression patterns of cell type-specific lncRNAs exclusively detected with ELATUS and unveiled biologically important lncRNAs, such as AL121895.1, a previously undocumented cis-repressor lncRNA, whose role in breast cancer progression was unnoticed by traditional methodologies. Our results emphasize the necessity for an alternative scRNA-seq workflow tailored to lncRNAs that sheds light on the multifaceted roles of lncRNAs.

bioinformatics↗

bioRxiv2023

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a framework based on a sparsely-connected autoencoder and a three-tier training that yields robust and interpretable GSAS. NetActivity was trained with 1,518 well-known gene sets and all GTEx samples, returning GSAS representative of the original transcriptome and assigning higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype.

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↗

bioRxiv2023

GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks

Drug-target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness. In this work, we introduce GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space. Code Availabilityhttps://github.com/ubioinformat/GeNNius

bioinformatics↗

bioRxiv2022

Identification of transcriptional network disruptions in drug-resistant prostate cancer with TraRe

Metastatic castration-resistant prostate cancer (mCRPC) presents very low survival rates due to lack of response or acquired resistance to the available therapies. To date no molecular mechanisms of resistance have been identified, pointing out their complex dynamics. To identify key genes and processes associated with phenotypically-driven regulatory differences, we developed TraRe, a computational method that provides a three-tier analysis: i) at the network level, inferring differentially regulated modules; ii) at the regulon level, identifying regulatory relationships linked to phenotypic differences; and iii) at the single gene level, identifying TFs consistently linked to rewired modules. We applied TraRe (available in Bioconductor with full documentation) to transcriptomic data from 46 mCRPC patients with Abiraterone-response clinical data and uncovered abrogated immune response regulatory modules that showed strong differential regulation in Abi-resistant patients. These modules were replicated in an independent mCRPC study. Further, we experimentally validated key rewiring predictions and their associated transcription factors. Among them, ELK3, MXD1, and MYB were found to have a differential role in cell survival for Abi-response-specific settings. Moreover, we identified the role of ELK3 in cell migration capacity, which could have direct impact on mCRPC. Collectively, these findings shed light on the underlying regulatory mechanisms driving abiraterone response.

bioinformatics↗

bioRxiv2022

Evolution of decreased aggression in honey bees targeted brain cell gene regulatory networks

Understanding how genotypic variation results in phenotypic variation, a major challenge in biology, is especially difficult for collective behaviour because collective group phenotypes arise from complex interactions between group members1. Honeybees aggressively defend their colony from attacks with highly integrated collective behaviour in which different groups of bees play specific roles, giving rise to distinct colony-level differences in aggression. A previous genome-wide association study of a population of Africanized honeybees (Apis mellifera scutellata) from Puerto Rico that recently evolved decreased aggression identified hundreds of genes with single nucleotide polymorphisms (SNPs) that associated with colony-level variation in aggression2. Many of these SNPs also showed strong signals of selection for decreased aggression2,3, but their influence on brain function was unknown. Using brain single-cell (sc) transcriptomics and sc gene regulatory network analysis, we show here that variants of these genes give rise to genetic differences in transcription factor-target gene relationships. These differences involved the activity of several TFs, some that have been previously associated with aggression, like single stranded-binding protein c31A, and some that have been associated with tissue morphogenesis but not behaviour, like apontic. The activity of these and other TFs was located in specific brain cell populations related to olfaction and vision, the two sensory modalities that bees use in colony defence. They also implicate metabolism of serotonin, a neurochemical already known to influence honeybee aggression, but not from a genetic perspective. Surprisingly, genetic differences were more pronounced in the brains of forager bees than in similarly aged but more aggressive soldier bees, pointing to an evolutionary change in division of labour for colony defence. Our results demonstrate how group genetics can shape a collective phenotype by modulating individual brain gene regulatory network architecture.

genomics↗

bioRxiv2021

Transcriptional regulation of HSCs in Aging and MDS reveals DDIT3 as a Potential Driver of Transformation

Myelodysplastic syndromes (MDS) are hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis, with increased incidence in elderly individuals. In this work, we analyzed the transcriptome of human HSCs purified from young and elderly healthy donors, as well as MDS patients, identifying transcriptional alterations following eight different patterns of expression. While aging-associated lesions seemed to predispose HSCs to myeloid transformation, disease-specific alterations may trigger MDS development. Among MDS-specific lesions, we detected the upregulation of the transcription factor DDIT3. Overexpression of DDIT3 in human healthy HSCs induced an MDS-like transcriptional state, and a delay in erythropoiesis. Such effect was associated with downregulation of transcription factors required for normal erythropoiesis, and with a failure in the activation of their transcriptional programs. Moreover, DDIT3 knockdown in CD34+ cells from MDS patients with anemia was able to restore erythropoiesis. These results identify DDIT3 as a driver of dyserythropoiesis, and a potential therapeutic target to restore the inefficient erythropoiesis characterizing MDS patients. STATEMENT OF SIGNIFICANCEThis study defines how human aging and MDS development are characterized by transcriptional alterations in HSCs that follow different patterns, some of which may contribute to myeloid transformation. Among them, we demonstrate how MDS-specific upregulation of DDIT3 in HSCs induces dyserythropoiesis, while its knockdown in HSPCs from MDS patients restores proper erythroid differentiation.

cancer biology↗

bioRxiv2021

Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single cell resolution

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single cell RNA sequencing (scRNAseq) to characterize an enriched population of human hematopoietic stem and progenitor cells (HSPC) obtained from young and elderly healthy individuals. Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain age-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRN regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2 and RUNX1 suggesting a role of these TF in the pathogenesis of the disease. In summary, we demonstrate that the combination of single cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

genomics↗

bioRxiv2020

JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

Single-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

bioinformatics↗

bioRxiv2020

Cellular plasticity balances the metabolic and proliferation dynamics of a regenerating liver

The adult liver has exceptional ability to regenerate, but how it sustains normal metabolic activities during regeneration remains unclear. Here, we use partial hepatectomy (PHx) in tandem with single-cell transcriptomics to track cellular transitions and heterogeneities of ~22,000 liver cells through the initiation, progression, and termination phases of mouse liver regeneration. Our results reveal that following PHx, a subset of hepatocytes transiently reactivates an early-postnatal-like gene expression program to proliferate, while a distinct population of metabolically hyperactive cells appears to compensate for any temporary deficits in liver function. Importantly, through combined analysis of gene regulatory networks and cell-cell interaction maps, we find that regenerating hepatocytes redeploy key developmental gene regulons, which are guided by extensive ligand-receptor mediated signaling events between hepatocytes and non-parenchymal cells. Altogether, our study offers a detailed blueprint of the intercellular crosstalk and cellular reprogramming that balances the metabolic and proliferation requirements of a regenerating liver.

genomics↗