Biology subjects

Nazari, P.

Publications and source records attributed to Nazari, P..

7 recordsLinked to original sources

bioRxiv2026

Longitudinal tumor ecosystem mapping defines glioblastoma treatment trajectories

Glioblastoma remains an invariably recurring and lethal brain tumor shaped by complex interactions between malignant and microenvironmental cells. How these interactions evolve under first-line standard-of-care (SOC) therapy remains unclear. We performed spatial single-cell profiling of 671 paired newly diagnosed and recurrent glioblastoma samples from 96 patients to map tumor-ecosystem evolution and its clinical relevance. We delineated five distinct patient subgroups, each characterized by unique ecosystem trajectories that correlated with clinical outcome. Patients whose tumors transitioned into oligodendrocyte-progenitor-like niches with enhanced vascular integrity, oxygenation and an IL10/CCL/MHCII immunomodulatory environment experienced improved clinical prognosis. In contrast, progression towards hypoxic mesenchymal/astrocyte-like niches dominated by strong LGALS1/SPP1/TGF{beta} immunosuppression predicted poor outcome. Furthermore, a subgroup of early-relapsing patients on SOC therapy that was characterized by a depletion of an antigen-presenting myeloid-cell-rich niche, exhibited diminished responsiveness to second-line lomustine therapy. Overall, mapping evolutions in glioblastoma ecosystems offers a novel framework for prognostic stratification and therapeutic guidance.

cancer biology↗

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↗

bioRxiv2025

Distinct tumor immune microenvironmental (TIME) landscapes drive divergent immunotherapy responses in glioblastoma

BackgroundImmunotherapies have improved outcomes in many cancers but show limited efficacy in glioblastoma (GBM). This study aimed to determine whether immunotherapy could be tailored to GBM by functional subtyping of vascular-immune landscapes. MethodsWe employed single-cell RNA sequencing, multiplex immunohistochemistry to characterize three distinct TIME subtypes in human and murine GBMs. We evaluated responses to combination of anti-angiogenic immunomodulating therapies (CD40 Ag, anti-PDL1, PI3K{gamma}/{delta} inhibition) in orthotopic syngeneic GBM mouse models. ResultsWe identified three distinct functional TIME subtypes with unique vascular-immune landscapes in human and murine GBM: TIME-low (immune-low/deserted, leaky vasculature), TIME-med (intermediate immune-infiltration, angiogenic), and TIME-high (heavily infiltrated with immunosuppressive myeloid cells and dysfunctional T cells). Representative mouse models of TIME-GBMs responded in subtype specific ways to anti-angiogenic immunomodulating therapies. TIME-low GBMs enhanced T-cell activity but relapsed due to emerging myeloid immunosuppression, concomitant with mesenchymal transition. TIME-med displayed the most immune-activated, yet angiogenic phenotype, and showed overall good responses to various anti-angiogenic immunomodulating therapies. TIME-high GBMs were mostly non-responsive but improved when the myeloid-cell PI3K{gamma} was targeted. However, CD40 agonist treatment, expected to enhance APC function, unexpectedly worsened survival by promoting angiogenesis and heightening immunosuppression, leading to dysfunctional T cells and reduced NK cell recruitment, and subsequent enhanced tumor propagation. ConclusionsOur study reveals three GBM TIME subtypes with distinct vascular-immune landscapes that require tailored therapies. TIME-med tumors are predicted to respond best to immunotherapies, TIME-low tumors show transient effects with anti-angiogenic immunomodulating therapies, while TIME-high tumors, due to their profound immunosuppression, can even have worse outcomes. Key pointsO_LIThree TIME subtypes were identified in GBM with distinct vascular-immune landscapes C_LIO_LITIME subtypes show divergent immunotherapy responses C_LIO_LITIME classification supports personalized treatment strategy for GBM immunotherapy C_LI Importance of the StudyThis study advances glioblastoma immunotherapy by providing the first comprehensive single-cell characterization of TIME subtypes, moving beyond bulk RNA-sequencing to reveal detailed functional states of immune cells. We establish clinically relevant murine models that recapitulate human TIME subtypes, enabling preclinical testing of TIME-targeted therapies. Our findings identify TIME-low GBM as immune deserted and TIME-med tumors as the most immunotherapy-responsive subtype that should be prioritized for clinical selection. We found that high immune infiltration correlates with non-responsiveness and even unexpected detrimental effects with CD40 agonist treatment in TIME-high tumors--critical information given ongoing clinical trials. Identifying distinct immunosuppressive mechanisms across TIME subtypes and differential treatment responses provides a framework for personalized immunotherapy selection. The immediate translational impact of this work highlights the importance of TIME classification for treatment stratification and the urgent need to consider TIME status in clinical trial design, potentially explaining variable patient responses in previous GBM immunotherapy trials.

cancer biology↗

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↗

bioRxiv2022

Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma

BackgroundFunctional profiling of freshly isolated glioblastoma cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity. MethodsHere, we describe the precision oncology by single-cell profiling using ex vivo readouts of functionality (PROSPERO) assay to evaluate the intrinsic susceptibility of high- grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution. ResultsThe PROSPERO assay was developed by correlating short-term single-cell molecular signatures using CyTOF to long-term cytotoxicity readouts in representative patient- derived glioblastoma cell cultures (n=14) that were exposed to radiotherapy and the small- molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n=34). Here, PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers upon ex vivo treatment exposure, while retaining proliferative capacity, insights that were validated in PDX models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal transitions, while in patients samples this was more heterogeneous. ConclusionPROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.

cancer biology↗

bioRxiv2022

Protective effects of different doses of MitoQ separately and combined with trehalose on sperm function and antioxidative status of cryopreserved Markhoz goat semen

The mitochondria-targeted antioxidant MitoQ has been regarded as an effective antioxidant agent against cryo-induced oxidative cellular damage. This study aimed to evaluate the use of different doses of MitoQ combined with trehalose to minimize mitochondrial impairment and oxidative stress during sperm cryopreservation of Markhoz goat. For this, semen collection was performed every 5 days from 5 bucks (10 ejaculates per buck). The ejaculates were pooled and then diluted in eight different Tris-based extenders as follows: no additives (control), 20, 200, 2000 nM of MitoQ (MT20, MT200, MT 2000, respectively), 150 mM of trehalose (Tr), MT20+Tr, MT200+Tr, MT2000+Tr. Each semen sample was frozen using a standard protocol, and sperm function and antioxidative status were evaluated after thawing. Results indicated higher total and progressive motility, acrosome and membrane integrity, superoxide dismutase, glutathione peroxidase, total antioxidant capacity, and lower DNA fragmentation and malondialdehyde in MT200+Tr than for all other groups except MT200; meanwhile, MT200 was also greater in these parameters than in the control group (P < 0.05). Furthermore, MT200 and MT200+Tr showed higher (P < 0.05) percentages of live cryopreserved sperm with high membrane mitochondrial potential than other groups. However, abnormality percentage and catalase activity of frozen-thawed sperm were not affected by treatments (P > 0.05). To conclude, we have found that supplementation of 200 nM MitoQ alone or in combination with 150 mM trehalose to semen extender improves the quality of cryopreserved sperm in goats, which is associated with enhanced antioxidant enzymatic defense and mitochondrial activity and reduced DNA fragmentation.

cell biology↗