Biology subjects

Casado, J.

Publications and source records attributed to Casado, J..

7 recordsLinked to original sources

bioRxiv2025

DiatomNet: An automatic Diatom genus identification system through microscopic images and Deep Learning

Diatoms are microscopic organisms belonging to the algae kingdom. They adapt to the ecosystem and modify their shape and texture depending on hundreds of ecosystem variables. Hence, these micro-organism are considered as the most accurate indicator to measure water quality. Commonly, the recognition of the class of diatoms in a microscopic image has always been done by expert biologists knowledgeable about the morphometric characteristics of these organisms. This work proposes a new automatic diatom genus recognition system from microscopic images using state-of-the-art deep CNNs. In particular, 1) we developed a public high quality database organized into 44 genus-level diatom classes, 2) designed a robust diatom classification model, and 3) provided a user-friendly interface to utilize our automatic diatom recognition tool.

bioinformatics↗

bioRxiv2024

Single-cell spatial atlas of high-grade serous ovarian cancer unveils MHC class II as a key driver of spatial tumor ecosystems and clinical outcomes

The tumor microenvironment (TME) is a complex network of interactions between malignant and host cells, yet its orchestration in advanced high-grade serous ovarian carcinoma (HGSC) remains poorly understood. We present a comprehensive single-cell spatial atlas of 280 metastatic HGSCs, integrating high-dimensional imaging, genomics, and transcriptomics. Using 929 single-cell maps, we identify distinct spatial domains associated with phenotypically heterogeneous cellular compositions, and demonstrate that immune cell co-infiltration at the tumor-stroma interface significantly influences clinical outcomes. To uncover the key drivers of the tumor ecosystem, we developed CEFIIRA (Cell Feature Importance Identification by RAndom forest), which identified tumor cell-intrinsic MHC-II expression as a critical predictor of prolonged survival, independent of clinicomolecular profiles. Validation with external datasets confirmed that MHC-II-expressing cancer cells drive immune infiltration and orchestrate spatial tumor-immune interactions. Our atlas offers novel insights into immune surveillance mechanisms across HGSC clinicomolecular groups, paving the way for improved therapeutic strategies and patient stratification.

cancer biology↗

bioRxiv2024

Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomicdata

MotivationMultiplexed imaging and single-cell analysis are increasingly applied to investigate the tissue spatial ecosystems in cancer and other complex diseases. Accurate single-cell phenotyping based on marker combinations is a critical but challenging task due to (i) low reproducibility across experiments with manual thresholding, and, (ii) labor-intensive ground-truth expert annotation required for learning-based methods. ResultsWe developed Tribus, an interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. We demonstrated that Tribus recovers fine-grained cell types, matching the gold standard annotations by human experts. Additionally, Tribus can target ambiguous populations and discover phenotypically distinct cell subtypes. Through benchmarking against three similar methods in four public datasets with ground truth labels, we show that Tribus outperforms other methods in accuracy and computational efficiency, reducing runtime by an order of magnitude. Finally, we demonstrate the performance of Tribus in rapid and precise cell phenotyping with two large in-house whole-slide imaging datasets. AvailabilityTribus is available at https://github.com/farkkilab/tribus as an open-source Python package.

bioinformatics↗

bioRxiv2023

Single-cell spatial atlas of tertiary lymphoid structures in ovarian cancer

BackgroundRecent advances in highly-multiplexed tissue technologies and image analysis tools have enabled a more detailed investigation of the tumor microenvironment (TME) and its spatial features, including tertiary lymphoid structures (TLSs), at single-cell resolution. TLSs play a major part in antitumor immune responses, however, their role in antitumor immunity in ovarian cancer remains largely unexplored. MethodsIn this study, we generated a comprehensive single-cell spatial atlas of TLSs in ovarian cancer by extracting spatial topology information from in-situ highly-multiplexed cellular imaging using tissue cyclic immunofluorescence (CyCIF). Our analysis included 44 patients with high-grade serous ovarian cancer (HGSC) from the TOPACIO Phase II clinical trial. We combined spatial and phenotypic features from 302,545 single-cells with histopathology, targeted sequencing-based tumor molecular groups, and Nanostring gene expression data. ResultsWe find that TLSs are associated with a distinct TME composition and gene expression profile, characterized by elevated levels of the chemokines CCL19, CCL21, and CXCL13 correlating with the number of TLSs in the tumors. Using single-cell feature quantification and spatial mapping, we uncover enriched germinal center (GC) B cell infiltration and selective spatial attraction to follicular helper T and follicular regulatory T cells in the TLSs from chemo-exposed and BRCA1 mutated HGSCs. Importantly, spatial statistics reveal three main groups of cell-to-cell interactions; significantly enriched structural compartments of CD31+ cells, myeloid, and stromal cell types, homotypic cancer cell- and cancer cell to IBA1+ myeloid cell crosstalk, and enriched selective Tfh, Tfr, and Tfc communities with predominant Tfh - GC B cell interactions. Finally, we report spatiotemporal gradients of GC-B cell interactions during TLS maturation, with enriched non-GC B cell attraction towards the GC B cells in early TLSs, and avoidance patterns with selective GC B-cell communities in the TLSs with GCs. ConclusionsOur single-cell multi-omics analyses of TLSs showed evidence of active adaptive immunity with spatial and phenotypic variations among distinct clinical and molecular subtypes of HGSC. Overall, our findings provide new insights into the spatial biology of TLSs and have the potential to improve immunotherapeutic targeting of ovarian cancer. What is already known on this topicTLSs play a major part in antitumor immune responses, however, their exact role and mechanisms in antitumor immunity are widely unexplored. What this study addsOur results deepen the understanding of TLS biology including cell-cell interactions and shows how the presence of TLSs is characterized with a distinct TME composition and gene expression profile. How this study might affect research, practice or policyOverall, our findings provide new insights into the spatial biology of TLSs and have the potential to improve therapeutic options for ovarian cancer.

cancer biology↗

bioRxiv2021

Optimized detection of somatic allelic imbalances specific for homologous recombination deficiency improves the prediction of clinical outcomes in ovarian cancer

Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in cancer. The accurate detection of somatic allelic imbalances (AIs) has been limited by methods focused on BRCA1/2 mutations and using mixtures of cancer types. Using pan-cancer data, we revealed distinct patterns of AIs in high-grade serous ovarian cancer (HGSC). We used machine learning and statistics to generate improved criteria to identify HRD in HGSC (ovaHRDscar). ovaHRDscar significantly predicted clinical outcomes in three independent patient cohorts with higher precision than previous methods. Characterization of 98 spatiotemporally distinct metastatic samples revealed low intra-patient variation and indicated the primary tumor as the preferred site for clinical sampling in HGSC. Further, our approach improved the prediction of clinical outcomes in triple-negative breast cancer (tnbcHRDscar), validated in two independent patient cohorts. In conclusion, our tumor-specific, systematic approach has the potential to improve patient selection for HR-targeted therapies.

cancer biology↗

bioRxiv2020

Agile workflow for interactive analysis of mass cytometry data

MotivationSingle-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single cell resolution. These large amounts of data, however, require dedicated, interactive tools for translating the data into knowledge. ResultsWe present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automatizes the use of of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood as well as cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AvailabilityThe method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto Contactsampsa.hautaniemi@helsinki.fi Supplementary informationSupplementary material is available and FCS files are hosted at flowrepository.org/id/FR-FCM-Z2LW

bioinformatics↗

bioRxiv2019

Epigenetic inhibitors sensitize DLBCL cells to rituximab and doxorubicin

Primary therapy for diffuse large B-cell lymphoma (DLBCL) is an immunochemotherapy regimen comprising rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). While R-CHOP cures 60% of the patients with DLBCL, those who do not respond or relapse have dismal prognosis, and effective treatment strategies are needed. Due to the plastic nature of the epigenome and its fundamental role in regulating cell identity, we hypothesized that reprogramming the epigenome can overcome resistance to R-CHOP. We developed a novel drug screening protocol to identify epigenetic modifiers that sensitize DLBCL cell lines to immunochemotherapy. Of the herein tested 60 epigenetic compounds, we identified several histone deacetylase (HDAC) and histone methyltransferase (HMT) inhibitors that acted synergistically with immunochemotherapy. We show that sensitization through HDAC and HMT inhibitors is achieved by dysregulating homologous recombination, a central DNA repair pathway, as well as by disrupting the cell cycle and affecting the apoptotic pathway. Epigenetic inhibitors are well-tolerated, which together with our findings support their use in combination with immunochemotherapy in patients with primary refractory and relapsed DLBCL.

cancer biology↗