Biology subjects

Antoranz, A.

Publications and source records attributed to Antoranz, A..

12 recordsLinked to original sources

bioRxiv2025

CLEAR-AF: Improved Autofluorescence Subtraction for Multiplexed Tissue Imaging via Polar Transformation and Gaussian Mixture Models

Cyclic Multiplexed ImmunoFluorescence (cMIF) can profile dozens to hundreds of proteins at subcellular resolution, offering unprecedented insights into tissue architecture in health and disease. However, measured signals are affected by tissue autofluorescence (AF), which can confound molecular quantifications at the single-cell level. Existing computational strategies to subtract AF are not sensitive nor specific enough and often leave residual background or over subtract weak markers. To address current limitations, we introduce CLEAR-AF (Coordinate-transformed Local Estimation and Adaptive Removal of AutoFluorescence), a subtraction framework where AF is calibrated for each acquired signal using a reference image. CLEAR-AF maps these intensities into polar coordinates to disentangle AF from true signal, and applies adaptive, distribution-aware thresholding to estimate and remove AF locally. Across multiple cMIF technologies, CLEAR-AF yields cleaner marker channels with improved specificity, greater sensitivity, and higher reproducibility relative to common AF removal approaches. By delivering more accurate per-cell measurements without workflow changes to acquisition, CLEAR-AF provides a practical, platform-agnostic step towards more reliable spatial proteomics at scale.

bioinformatics↗

bioRxiv2025

CONCLAVE: CONsensus CLustering with Annotation-Validation Extrapolation for cyclic multiplexed immunofluorescence data

High-dimensional cyclic multiplexed immunofluorescence (cMIF) enables single-cell phenotyping within intact tissues. Cell annotations rely on a multi-step pipeline involving normalization, sampling, dimensionality reduction, and clustering, but the absence of standardized benchmarks for method selection--especially at the clustering stage--leads to inconsistent and less reproducible phenotyping. To address this, we developed CONCLAVE, a consensus-clustering-based workflow that optimizes upstream steps and integrates results from multiple clustering algorithms retaining only those cell labels supported by at least two independent methods. Through in-silico simulations and real-world cMIF datasets, CONCLAVE consistently outperformed single-clustering-method approaches in accuracy, reproducibility, and robustness, with improvements becoming more evident when mapped within spatial tissue contexts. Additionally, CONCLAVE includes a scoring module that flags regions likely to contain unreliable or inconsistent data, facilitating targeted quality control. In summary, CONCLAVE offers a robust framework for cell annotation in cMIF datasets, enhancing the reliability of downstream spatial proteomics analyses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/688190v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@14ddfecorg.highwire.dtl.DTLVardef@1a83a17org.highwire.dtl.DTLVardef@17dfc74org.highwire.dtl.DTLVardef@493c07_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

COLLAGE: COnsensus aLignment of muLtiplexing imAGEs

Multiplexed immunohistochemistry (mIHC) enables the high-dimensional single-cell interrogation of pathological tissue samples. mIHC is commonly based on the collection of high-resolution images from repeated staining cycles of the same tissue sample. Images of individual cycles typically consist of smaller tiles that need to be stitched into larger composite images, while images from serial rounds require alignment in a shared set of coordinates to enable pixel-perfect data integration. Current algorithms for stitching and registration require solving a single large puzzle consisting of billions of pixels making them computationally expensive but moreover forcing them to introduce errors to close the puzzle, which significantly impact the downstream results and the single-cell profiles. Here, we present the development and evaluation of COLLAGE (COnsensus aLignment of muLtiplexing imAGEs), an innovative stitching and registration method that leverages on the complementarity of these two steps in a divide and conquer approach: in contrast to other algorithms, COLLAGE breaks the process down into thousands of small puzzles, enabling extensive parallelisation and not forcing errors in its solution. Because COLLAGE also includes AlgnQC, a novel deep-learning-based evaluation metric of registration quality, the quality of the resulting image stacks is consistently maximised, while images with errors are flagged in an automated way. COLLAGE is available via www.disscovery.org. O_FIG O_LINKSMALLFIG WIDTH=148 HEIGHT=200 SRC="FIGDIR/small/603557v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@18d0f5borg.highwire.dtl.DTLVardef@1eb2756org.highwire.dtl.DTLVardef@163bb95org.highwire.dtl.DTLVardef@b04453_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

QUAL-IF-AI: Quality Control of Immunofluorescence Images using Artificial Intelligence

Fluorescent imaging has revolutionized biomedical research, enabling the study of intricate cellular processes. Multiplex immunofluorescent imaging has extended this capability, permitting the simultaneous detection of multiple markers within a single tissue section. However, these images are susceptible to a myriad of undesired artifacts, which compromise the accuracy of downstream analyses. Manual artifact removal is impractical given the large number of images generated in these experiments, necessitating automated solutions. Here, we present QUAL-IF-AI, a multi-step deep learning-based tool for automated artifact identification and management. We demonstrate the utility of QUAL-IF-AI in detecting four of the most common types of artifacts in fluorescent imaging: air bubbles, tissue folds, external artifacts, and out-of-focus areas. We show how QUAL-IF-AI outperforms state-of-the-art methodologies in a variety of multiplexing platforms achieving over 85% of classification accuracy and more than 0.6 Intersection over Union (IoU) across all artifact types. In summary, this work presents an automated, accessible, and reliable tool for artifact detection and management in fluorescent microscopy, facilitating precise analysis of multiplexed immunofluorescence images.

bioinformatics↗

bioRxiv2023

NK cells contribute to resistance to anti-PD1 therapy in immune-excluded melanomas

Immune checkpoint blockade (ICB) has become a standard of care in the treatment of metastatic melanoma (MM). Although ICB is particularly successful in some MM patients, more than half do not obtain a durable benefit. Biomarkers that predict response are urgently needed and overcoming intrinsic resistance is key to improving the success of ICB therapy. Using single cell RNA sequencing, we characterized the immune landscape of pre- and early on-treatment biopsies taken from a cohort of MM patients (n>20) exposed to ICB therapy. Our analysis identified >20 immune cell types and confirmed previously described associations between the abundance of various CD8 T cell populations and ICB outcome. Unexpectedly, we found that lack of response was associated with an increased occurrence of a granulysin-expressing (GNLY+) natural killer (NK) cell population. This observation was replicated in other MM cohorts and in a breast cancer cohort in which paired biopsies were also collected pre and early-on ICB therapy. Spatial proteomics revealed that whereas NK cells colocalized with CD8 T cells within the tumour bed in responding lesions, these cells accumulated at the tumour margin in non-responding lesions. Strikingly, depletion of NK cells in an NRAS-driven melanoma mouse model, which exhibits an immune-excluded phenotype and is refractory to ICB, promoted massive immune cell infiltration and tumour clearance upon anti-PD1 exposure. These data highlight a differential immune cell topography between early on-treatment responding and nonresponding MM lesions, which could be exploited to develop a robust stratification biomarker, and unravel an unexpected contribution of NK cells in primary resistance to ICB.

cancer biology↗

bioRxiv2023

ELITE: Expression deconvoLution using lInear optimizaTion in bulk transcriptomics mixturEs

Understanding the cellular composition of tissue samples is crucial for identifying the molecular mechanisms underlying diseases and developing cellular targets for therapeutic interventions. Digital cytometry methods have been developed to predict tissue composition from bulk transcriptomic data, avoiding the high cost associated with single-cell profiling. Here, we present ELITE, a new digital cytometry method that utilizes linear programming to solve the deconvolution problem. ELITE uses as inputs a mixture matrix representing bulk measurements, and a signature matrix representing molecular fingerprints of the cell types to be identified. The signature matrix can be obtained from single-cell datasets or the literature, making ELITE more flexible than other methods that rely solely on single-cell data. We evaluated ELITE on three publicly available single-cell datasets and compared it with five other deconvolution methods, showing superior performance, particularly when there were cell types with similar expression profiles. As a case study, we evaluated the prediction of tumor cellularity using purity estimates from 20 different TCGA carcinoma datasets.

bioinformatics↗

bioRxiv2022

A TCF4/BRD4-dependent regulatory network confers cross-resistance to targeted and immune checkpoint therapy in melanoma

Primary resistance drastically limits the clinical success of immune checkpoint blockade (ICB) in melanoma. Resistance to ICB may also develop when tumours relapse after targeted therapy. To identify cancer cell-intrinsic mechanisms driving resistance to ICB, we generated single-cell RNA-sequencing (scRNA-seq) data from a prospective longitudinal cohort of patients on ICB therapy, including an early time point obtained after only one cycle of treatment. Comparing these data with murine scRNA-seq datasets, we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem, and defined 6 evolutionarily conserved melanoma transcriptional metaprograms (Melanocytic or MEL, Mesenchymal-like or MES, Neural Crest-like, Antigen Presentation, Stress (hypoxia response) and Stress (p53 response)). Spatial multi-omics revealed a non-random geographic distribution of cell states that is, at least partly, driven by the tumour microenvironment. The single-cell data allowed unambiguous discrimination between melanoma MES cells and cancer-associated fibroblasts both in silico and in situ, a long-standing challenge in the field. Importantly, two of the melanoma transcriptional metaprograms were associated with divergent clinical responses to ICB. While the Antigen Presentation cell population was more abundant in tumours from patients who exhibited a clinical response to ICB, MES cells were significantly enriched in early on-treatment biopsies from non-responders, and their presence significantly predicted lack of response. Critically, we identified TCF4 (E2-2) as a master regulator of the MES program and suppressor of both MEL and Antigen Presentation programs. Targeting TCF4 expression in MES cells either genetically or pharmacologically using a bromodomain inhibitor increased immunogenicity and sensitivity to targeted therapy. This study describes an increasingly complex melanoma transcriptional landscape and its rapid evolution under ICB. It also identifies a putative biomarker of early response to ICB and an epigenetic therapeutic strategy that increases both immunogenicity of ICB-refractory melanoma and their sensitivity to targeted therapy.

cancer biology↗

bioRxiv2021

ELIMINATOR: Essentiality anaLysIs using MultIsystem Networks And inTeger prOgRamming

A gene is considered as essential when it is indispensable for cells to grow and replicate under a certain environment. However, gene essentiality is not a structural property but rather a contextual one, which depends on the specific biological conditions affecting the cell. This circumstantial essentiality of genes is what brings the attention of scientist since we can identify genes essential for cancer cells but not essential for healthy cells. This same contextuality makes their identification extremely challenging. Huge experimental efforts such as Project Achilles where the essentiality of thousands of genes is measured in over one thousand cell lines together with a plethora of molecular data (transcriptomics, copy number, mutations, etc.) can shed light on the causality behind the essentiality of a gene in a given environment by associating the measured essentiality to molecular features of the cell line. Here, we present an in-silico method for the identification of patient-specific essential genes using constraint-based modelling (CBM). Our method expands the ideas behind traditional CBM to accommodate multisystem networks, that is a biological network that focuses on complex interactions within several biological systems. In essence, it first calculates the minimum number of non-expressed genes required to be active by the cell to sustain life as defined by a set of requirements; and second, it performs an exhaustive in-silico gene knockout to find those that lead to the need of activating extra non-expressed genes. We validated the proposed methodology using a set of 452 cancer cell lines derived from the Cancer Cell Line Encyclopedia where an exhaustive experimental large-scale gene knockout study using CRISPR (Achilles Project) evaluates the impact of each removal. We also show that the integration of different essentiality predictions per gene, what we called Essentiality Congruity Score, (derived from multiple pathways) reduces the number of false positives. Finally, we explored the gene essentiality predictions for a breast cancer patient dataset, and our results showed high concordance with previous publications. These findings suggest that identifying genes whose activity are fundamental to sustain cellular life in a patient-specific manner is feasible using in-silico methods. The patient-level gene essentiality predictions can pave the way for precision medicine by identifying potential drug targets whose deletion can induce death in tumour cells.

bioinformatics↗

bioRxiv2020

A fully defined pluripotent stem cell derived multi-liver-cell model for steatohepatitis and fibrosis

Chronic liver injury, as observed in non-alcoholic steatohepatitis (NASH), progressive fibrosis, and cirrhosis, remains poorly treatable. Steatohepatitis causes hepatocyte loss in part by a direct lipotoxic insult, which is amplified by derangements in the non-parenchymal cellular (NPC) interactive network wherein hepatocytes reside, including, hepatic stellate cells, liver sinusoidal endothelial cells and liver macrophages. To create an in vitro culture model encompassing all these cells that allows studying liver steatosis, inflammation and fibrosis caused by NASH, we here developed a fully defined hydrogel microenvironment, termed hepatocyte maturation (HepMat) gel, that supports maturation and maintenance of pluripotent stem cell (PSC)-derived hepatocyte- and NPC-like cells for at least one month. The HepMat-based co-culture system modeled key molecular and functional features of TGF{beta}-induced liver fibrosis and fatty-acid induced inflammation and fibrosis better than monocultures its constituent cell populations. The novel co-culture system should open new avenues for studying mechanisms underlying liver steatosis, inflammation and fibrosis as well as for assessing drugs counteracting these effects.

bioengineering↗

bioRxiv2020

A Pan-Cancer Blueprint of the Heterogeneous Tumour Microenvironment Revealed by Single-Cell Profiling

AO_SCPLOWBSTRACTC_SCPLOWThe stromal compartment of the tumour microenvironment consists of a heterogeneous set of tissue-resident and tumour-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer (n=36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumour-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumours treated with checkpoint immunotherapy and identifying a naive CD4+ T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate a first panoramic view on the shared complexity of stromal cells in different cancers.

cancer biology↗

bioRxiv2018

High-dimensional single cell-analysis of lymphocytic activation in melanoma

In melanoma, the lymphocytic infiltrate is a prognostic parameter classified morphologically into "brisk", "non-brisk" and "absent" entailing a functional association that has never been proved. Recently, it has been shown that lymphocytic populations can be very heterogeneous, and that anti-PD-1 immunotherapy supports activated T cells. Here, we characterize the immune landscape in primary melanoma by high-dimensional single cell multiplex analysis in tissue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics. We observed that the brisk and non-brisk patterns are heterogeneous functional categories that can be further sub-classified into active, transitional or exhausted. The classification of primary melanomas based on the functional paradigm also shows correlation with spontaneous regression, and an improved prognostic value than that of the brisk classification. Finally, the main inflammatory cell subpopulations that are present in the microenvironment associated with activation and exhaustion and their spatial relationships are described using neighbourhood analysis.

cancer biology↗

bioRxiv2018

A functional landscape of chronic kidney disease entities from public transcriptomic data

To develop efficient therapies and identify novel early biomarkers for chronic kidney disease an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in CKD origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for nine kidney disease entities that account for a majority of CKD worldwide. We included data from five distinct studies and compared glomerular gene expression profiles to that of non-tumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms and conditions, that we mitigated with a bespoke stringent procedure. This allowed us to perform a global transcriptome-based delineation of different kidney disease entities, obtaining a landscape of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. Furthermore, we derived functional insights by inferring activity of signaling pathways and transcription factors from the collected gene expression data, and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating e.g. that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in RPGN whereas not expressed in control kidney tissue. These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities, that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate this, we provide our results as a free interactive web application: https://saezlab.shinyapps.io/ckd_landscape/. Translational StatementChronic kidney disease is a combination of entities with different etiologies. We integrate and analyse transcriptomics analysis of glomerular from different entities to dissect their different pathophysiology, what might help to identify novel entity-specific therapeutic targets.

pathology↗