Biology subjects

Carmona, S. J.

Publications and source records attributed to Carmona, S. J..

16 recordsLinked to original sources

bioRxiv2026

IL-23 licenses the transition from preclinical autoimmunity to inflammatory arthritis through IL-17A-dependent mechanisms

Objectives: The mechanisms driving transition from systemic autoimmunity to clinical rheumatoid arthritis (RA) remain poorly understood. We investigated the role of interleukin-23 (IL-23) in disease transition using a murine model of experimental arthritis, complemented by human transcriptomic analyses in individuals at risk of RA and RA synovial tissue. Methods: Collagen-induced arthritis (CIA) was induced in less susceptible C57BL/6 mice and combined with sustained systemic IL-23 overexpression. Disease severity, histopathology, immune responses, and tissue gene expression were evaluated. The contribution of IL-17A was assessed using a neutralizing anti-IL-17A antibody. Expression of IL-23 pathway-related genes was assessed in whole-blood transcriptomic data from a large cohort of first-degree relatives of patients with RA (n=1080) and in an independent synovial RNA-sequencing dataset (n=218). Results: In mice, sustained IL-23 overexpression converted a mild CIA phenotype into a highly penetrant arthritis characterized by increased expression of innate inflammatory mediators, and enhanced antigen-specific production of IL-17A and IFN-{gamma}, despite unchanged collagen-specific antibody responses. Neutralization of IL-17A prevented arthritis development. In human whole blood longitudinal analyses, lower RORC and KLRB1 expression was associated with a higher risk of RA onset. KLRB1 expression was persistently lower in progressors, whereas RORC expression showed a steeper decline before diagnosis, although this difference did not remain significant after correction for multiple testing. In RA synovium, IL23R, IL23A, and CCR2 expression was increased compared with controls, while IL23R expression was higher in early than established RA. Conclusions: IL-23 acts as a critical regulator of the transition from systemic autoimmunity to inflammatory arthritis by promoting IL-17A-dependent cellular and myeloid inflammatory pathways. The change in IL-23 pathway signatures in at-risk individuals and early RA supports the translational relevance of this pathway during disease initiation.

immunology↗

bioRxiv2026

Evaluating cell type annotations in single-cell omics in the absence of ground truth

Accurate cell type annotation is essential for single-cell transcriptomics, directly shaping downstream analyses and biological interpretations. Yet, objective evaluation of annotation quality remains a major challenge. Here, we argue that a cell type or cell state label has practical utility only if it captures a molecular pattern that is reproducible across biological replicates. Based on this principle, we introduce inter-sample consistency (ISC), a quantitative framework to assess annotation quality in single-cell RNA-seq datasets. Unlike existing cluster validation approaches, ISC distinguishes annotations that generalize across samples and individuals from those driven by technical or unwanted variation, thereby providing principled criteria for annotation quality and transferability. When applied to published single-cell atlases, ISC reveals widespread reproducibility gaps and provides actionable guidance for repairing inconsistent annotations. Notably, ISC enables benchmarking of automated cell type annotation tools even when ground-truth labels are unavailable, providing interpretable metrics to guide their development and evaluation. Implemented as the scTypeEval Bioconductor package, this framework offers a broadly applicable resource for evaluating and improving cell type annotations in single-cell RNA-seq experiments.

bioinformatics↗

bioRxiv2025

Metabolic reinvigoration of NK cells by IL-21 enhances immunotherapy against MHC-I deficient solid tumors

Natural killer (NK) cells, a type of potent cytotoxic lymphocytes, are particularly promising for the treatment of cancers that lose or downregulate major histocompatibility complex class I (MHC-I) expression to evade T cell-mediated immunotherapy. However, the hostile and immune suppressive tumor microenvironment (TME) greatly hinders the function of tumor-infiltrating NK cells limiting the therapeutic efficacy against solid tumors. Here, we show that a fusion protein of interleukin-21 (IL-21-Fc), as a direct in vivo intervention, can safely and effectively reprogram NK cell metabolism and enhance their effector function in the TME. Our research demonstrates that combining IL-21-Fc with IL-15 superagonist (IL-15SA) or NK cell transfer leads to the eradication of MHC-I-deficient tumors and confers durable protection in syngeneic and xenograft tumor models. Mechanistically, we uncover that IL-21-Fc enhances NK cell effector function by upregulating glycolysis in a lactate dehydrogenase A (LDHA)-dependent manner. These findings not only underscore the considerable potential of IL-21-Fc as an in vivo therapeutic intervention to bolster NK cell-based immunotherapy, but also unveil an innovative strategy of metabolic reprogramming for NK cell rejuvenation within tumors. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/643961v2_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@daf24forg.highwire.dtl.DTLVardef@5a5029org.highwire.dtl.DTLVardef@144c978org.highwire.dtl.DTLVardef@106ed1c_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LINK cells display functional exhaustion in MHC-I deficient solid tumors C_LIO_LIIL-21-Fc as an in vivo-applicable and safe immunotherapy reinvigorates intratumoral NK cells for enhanced effector function C_LIO_LIIL-21-Fc enhances NK cell function by elevating glycolysis in a LDHA-dependent manner C_LIO_LICombining IL-21-Fc with low-dose IL-15SA or NK cell transfer eradicates syngeneic and xenografted solid tumors C_LI

cancer biology↗

bioRxiv2025

ECM Signatures Reveal Quiescent Stem Cell Diversity in the Colonic Niche

Colonic stem cells have a key role in the continuous regeneration of a healthy intestinal epithelium. Despite considerable advances in single-cell omics technologies, the transcriptional heterogeneity of rare cell types such as colonic stem cells, as well as their functional states and niche-specific behaviors, remain poorly characterised. In this study, we leverage a meta-analysis of scRNA-seq and spatial transcriptomic datasets to comprehensively map the heterogeneity of colonic stem cells. We identify multiple, previously underappreciated stem cell states, including distinct quiescent subtypes marked by CDKN1A (P21), CDKN1B (P27), and CDKN1C (P57), proliferative populations defined by MKI67 (Ki67) and LRIG1, and a lineage-committed intermediate subpopulation expressing MUC2. Strikingly, we find that these states can be robustly identified solely by their extracellular matrix (ECM) gene expression signatures, revealing ECM composition as a critical determinant of stem cell identity. Notably, LAMA1 expression is highly specific to the P57+ quiescent population, linking laminin-mediated microenvironments to the active maintenance of deep quiescence, consistent with our recent findings associating LAMA1 with quiescent cell survival. By applying these ECM gene signatures, we delineate discrete "micro-niches" of quiescent stem cells in healthy tissue and provide evidence that analogous states persist in the colorectal cancer (CRC) environment. Extending our approach to an unrelated tissue, the pancreas, we detect parallel quiescent cell subtypes, illustrating the broader applicability of ECM-based signatures. Taken together, our findings redefine the concept of stem cell heterogeneity in the colon, establish ECM-driven gene signatures as a powerful tool for characterizing stem cell states, and offer new perspectives on the niche-dependent regulation of both healthy and cancerous stem cell populations.

developmental biology↗

bioRxiv2024

Wounding triggers invasive progression in human basal cell carcinoma

The interconnection between wound healing and cancer has long been recognized, as epitomized by the expression "cancer is a wound that does not heal". However, the impact of inducing a wound, such as through biopsy collection, on the progression of established tumors remains largely unknown. In this study, we apply single-cell spatial transcriptomics to characterize the heterogeneity of human basal cell carcinoma (BCC) and identify a wound response gene program as the most prominent feature of highly invasive BCC. To explore the causal relationship between wounding and cancer invasive progression, we perform a longitudinal experiment to compare human tumors at baseline and one week post-biopsy. Our results demonstrate that biopsy collection triggers, in proximity of the wound, the same transcriptional cancer cell state observed in highly invasive BCC. This cancer cell transcriptional switch is coupled with morphological changes and the transcriptional reprogramming of cancer-associated fibroblasts (CAFs). This study provides evidence that wounding triggers invasive progression of established human tumors and warrants further research on the potentially harmful effects of biopsies and wound-inducing treatments.

cancer biology↗

bioRxiv2023

Semi-supervised integration of single-cell transcriptomics data

Single-cell sequencing technologies offer unprecedented opportunities to characterize the complexity of biological samples with high resolution. At the same time, variations in sample processing and experimental protocols introduce technical variability - or "batch effects" - in the molecular readouts, hindering comparative analyses across samples and individuals. Although batch effect correction methods are routinely applied in single-cell omics analyses, data integration often leads to overcorrection, resulting in the loss of true biological variability. In this study, we present STACAS v2, a semi-supervised scRNA-seq data integration method that leverages prior knowledge in the form of cell type annotations to preserve biological variance. Through an open and reproducible benchmarking pipeline, we show that semi-supervised STACAS outperforms popular unsupervised methods such as Harmony, FastMNN, Seurat v4, scVI, and Scanorama, as well as supervised methods such as scANVI and scGen. Notably, STACAS is robust to incomplete and imprecise cell type annotations, which are commonly encountered in real-life integration tasks. Highlighting its scalability, we successfully applied semi-supervised STACAS to construct a high-resolution map of tumor-infiltrating CD8 T cells encompassing over 500,000 cells from 265 individuals. Based on our findings, we argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction. STACAS seamlessly integrates with Seurat pipelines and can be run with one command: Run.STACAS(seurat.list, cell.labels).

bioinformatics↗

bioRxiv2022

NFAT5 induction by the tumor microenvironment enforces CD8 T cell exhaustion

Persistent exposure to antigen during chronic infection or cancer renders T cells dysfunctional. The molecular mechanisms regulating this state of exhaustion are thought to be common in infection and cancer, despite obvious differences in their microenvironments. We discovered that NFAT5, an NFAT family member lacking an AP-1 docking site, is highly expressed in exhausted T cells from murine and human tumors and is a central player in tumor-induced exhaustion. While NFAT5 overexpression reduced tumor control, NFAT5 deletion improved tumor control by promoting the accumulation of tumor-specific CD8+ T cells that expressed less TOX and PD-1 and produced more cytokines particularly among precursor exhausted cells. Conversely, NFAT5 had no effect on chronic infection-induced T cell exhaustion. Mechanistically we found that TCR triggering induced NFAT5 expression and that hyperosmolarity stimulated transcriptional activity of NFAT5. We propose that NFAT5 takes over NFAT1/2 to promote exhaustion specifically in tumor-infiltrating CD8+ T cells.

immunology↗

bioRxiv2022

Orthogonal Gene Engineering Enables CD8+ T Cells to Control Tumors through a Novel PD-1+ TOX-indifferent Synthetic Effector State

Adoptive immunotherapy offers opportunities to reprogram T cells and the tumor microenvironment. Orthogonal engineering of adoptively transferred T cells with an IL-2R{beta}{gamma}-binding IL-2 variant, PD1-decoy and IL-33 led to cell-autonomous T-cell expansion, T-cell engraftment and tumor control in immunocompetent hosts through reprogramming of both transferred and endogenous CD8+ cells. Tumor-infiltrating lymphocytes adopted a novel effector state characterized by TOX suppression and specific expression of multiple effector molecules, most prominently granzyme C. While the IL-2 variant promoted CD8+ T-cell stemness and persistence, and was associated with downregulation of TOX, the combination with IL-33 was necessary to trigger the novel polyfunctional effector state. Rational T-cell engineering without host lymphodepletion enables optimal reprogramming of adoptively transferred T cells as well as mobilization of endogenous immunity into new functional CD8+ states mediating tumor control.

immunology↗

bioRxiv2021

scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

AO_SCPLOWBSTRACTC_SCPLOWA common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. In our benchmark for blood-derived and tumor-infiltrating immune cells, scGate outperforms SingleR, a state-of-the-art classifier for single-cell data. scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from complex scRNA-seq datasets. AvailabilityR package source code and reproducible tutorials are available at https://github.com/carmonalab/scGate

bioinformatics↗

bioRxiv2021

A single-cell reference atlas delineates CD4+ T cell subtype-specific adaptation during acute and chronic viral infections

CD4+ T cells are critical orchestrators of immune responses against a large variety of pathogens, including viruses. The multifaceted roles of CD4+ T cells, including their help to innate cells, CD8+ T and B cells and their support for long-lived immunity rely on a profound functional heterogeneity. While multiple CD4+ T cell subtypes and their key transcriptional regulators have been identified, there is a lack of consistent definition for CD4+ T cell transcriptional states. In addition, the progressive changes affecting CD4+ T cell subtypes during and after immune responses remain poorly defined. Taking advantage of single-cell transcriptomics, efficient computational methods, and robust animal models, we characterize the transcriptional landscape of CD4+ T cells responding to self-resolving and chronic viral infections. We build a comprehensive atlas of virus-specific CD4+ T cells and their evolution over time, and identify six major distinct cell states that are consistently observed in acute and chronic infections. During the course of acute infection, T cell composition progressively changes from effector to memory states, with subtype-specific gene modules and kinetics. Conversely, T cells in persistent infections fail to transition from effector to memory states, and acquire distinct, chronicity-associated transcriptional programs. By single-cell T cell receptor (TCR) sequencing analysis, we characterize the clonal structure of virus-specific CD4+ T cells across individuals and T cell subtypes. We find that virus-specific CD4+ T cell responses are mainly private across individuals and that most T cells differentiate into all subtypes independently of their TCR, in both acute and chronic infections. Finally, we show that our CD4+ T cell atlas can be used as a reference to accurately interpret cell states in external single-cell datasets. Overall, this study describes a previously unappreciated level of adaptation of the transcriptional states of CD4+ T cells responding to viruses and provides a new computational resource for CD4+ T cell analysis, available online at https://spica.unil.ch.

immunology↗

bioRxiv2021

Super-cells untangle large and complex single-cell transcriptome networks

The exponential scaling of scRNA-seq data represents an important hurdle for downstream analyses. Here we develop a coarse-graining framework where highly similar cells are merged into metacells. We demonstrate that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

bioinformatics↗

bioRxiv2021

APRANK: computational prioritization of antigenic proteins and peptides from complete pathogen proteomes

Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize antigenic targets in a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We applied this method to prioritize potentially antigenic proteins and peptides in a number of bacteria and protozoa causing human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). We have tested this integrative method using non-parametric ROC-curves and made an unbiased validation using an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.

immunology↗

bioRxiv2021

UCell: robust and scalable single-cell gene signature scoring

UCell is an R package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with Seurat objects. The UCell package and documentation are available on GitHub at https://github.com/carmonalab/UCell

bioinformatics↗

bioRxiv2020

Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses

Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary technology for characterizing the heterogeneity of cell populations. However, robust reference atlases that can be used to systematically interpret cellular states across studies and diseases are currently lacking. Here, we generated the first cross-study T cell atlases for cancer and viral infection and developed a novel algorithm, ProjecTILs, that enables the projection of new scRNA-seq data onto these reference atlases. ProjecTILs accurately predicted the effects of multiple perturbations, including the ablation of immunoregulatory targets controlling T cell differentiation, such as Tox, Ptpn2, miR-155 and Regnase-1, and suggested novel gene programs that were altered in these cells. Moving beyond mouse models, we used ProjecTILs to conduct a meta-analysis of human tumor-infiltrating T lymphocytes (TILs), revealing a remarkable conservation of TIL subtypes between human and mouse and across cancer types. Clonotype analysis supported a model in which rare human tumor-specific effector-memory (EM)-like CD8 TILs that resemble blood-circulating EM cells, differentiate into proliferative terminal exhausted/dysfunctional effector TILs through a progenitor subtype that upregulates the exhaustion master regulator TOX. Our novel computational method allows exploring the effect of human and murine T cell perturbations (e.g. as the result of therapy or genetic engineering) in terms of reference cellular states, altered genetic programs and clonotype structure, revealing mechanisms of action behind immunotherapies and opening opportunities for their improvement.

bioinformatics↗

bioRxiv2020

STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

Computational tools for the integration of single-cell transcriptomics data are designed to correct batch effects between technical replicates or different technologies applied to the same population of cells. However, they have inherent limitations when applied to heterogeneous sets of data with moderate overlap in cell states or sub-types. STACAS is a package for the identification of integration anchors in the Seurat environment, optimized for the integration of datasets that share only a subset of cell types. We demonstrate that by i) correcting batch effects while preserving relevant biological variability across datasets, ii) filtering aberrant integration anchors with a quantitative distance measure, and iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. We anticipate that the algorithm will be a useful tool for the construction of comprehensive single-cell atlases by integration of the growing amount of single-cell data becoming available in public repositories. Code availabilityR package: https://github.com/carmonalab/STACAS Docker image: https://hub.docker.com/repository/docker/mandrea1/stacas_demo

bioinformatics↗

bioRxiv2019

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq

Recent studies have proposed that tumor-specific tumor-infiltrating CD8+ T lymphocytes (CD8 TIL) can be classified into two main groups: \"exhausted\" TILs, characterized by high expression of the inhibitory receptors PD-1 and TIM-3 and lack of transcription factor 1 (Tcf1); and \"memory-like\" TILs, with self-renewal capacity and co-expressing Tcf1 and PD-1. However, a comprehensive definition of the heterogeneity existing within both tumor-specific and total CD8 TILs has yet to be clearly established.\n\nTo investigate this heterogeneity at the transcriptomic level, we performed paired single-cell RNA and TCR sequencing of CD8 T cells infiltrating B16 murine melanoma tumors, including cells of known tumor specificity. Unsupervised clustering and gene signature analysis revealed four distinct CD8 TIL states - exhausted, memory-like, naive and effector memory-like (EM-like) - and predicted novel markers, including Ly6C for the EM-like cells, that were validated by flow cytometry. Tumor-specific PMEL T cells were predominantly found within the exhausted and memory-like states but also within the EM-like state. Further, TCR repertoire sequencing revealed a large clonal expansion of exhausted, memory-like and EM-like cells with partial clonal relatedness between them. Finally, meta-analyses of public bulk and single-cell RNA-seq data suggested that anti-PD-1 treatment induces expansion of EM-like cells.\n\nOur reference map of the transcriptomic landscape of murine CD8 TILs will help interpreting future bulk and single-cell transcriptomic studies and may guide the analysis of CD8 TIL subpopulations in response to therapeutic interventions.

immunology↗