Biology subjects

Gottardo, R.

Publications and source records attributed to Gottardo, R..

At least 19 recordsLinked to original sources

bioRxiv2026

An eicosanoid-enriched follicular microenvironment shapes germinal centre immune dynamics in antiretroviral-treated PLWH

Delineating the follicular (F) cellular and molecular landscape in HIV infection is essential for understanding neutralizing antibody responses and HIV reservoir maintenance. Multiplex imaging analysis revealed a less differentiated profile for follicular helper CD4 T cells (TFH) and significantly increased follicular CD25lo/hiFoxp3hi T cells in lymph nodes (LNs) from antiretroviral treated (cART) compared to viremic (Vir) people living with HIV (PLWH). Spatial transcriptomics identified distinct inflammatory follicular microenvironments in Vir and cART LNs, characterized by interferon and eicosanoid enrichment, respectively. In an independent cohort, scRNA-sequencing analysis of LN-derived cells revealed an enrichment of eicosanoid-related pathways in follicular immune cell types in non-neutralizers compared to neutralizers PLWH. In vitro infection and in situ multimodal investigation of HIV DNA+ cell microenvironments suggested a potential role of PGE2/Eicosanoids in maintaining viral reservoirs in cART LNs. Our data highlight cellular and molecular factors that could regulate both antibody responses and viral reservoir persistence in PLWH.

immunology↗

bioRxiv2026

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a (latent) flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKras^G12D allele in silico. In total, we evaluated Phoenix on over 10,000 patients. Our results establish virtual spatial transcriptomics as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

bioinformatics↗

bioRxiv2026

Deciphering spatial heterogeneity of patient-derived organoids of colorectal tumors for drug discovery

The spatial organization of tumors is critical for drug development, as spatial heterogeneity in cancer cells and the tumor microenvironment can present a significant barrier to effective therapies. Patient-derived tumor organoids (PDOs) offer promising platforms to study tumor biology and for drug development. Previous studies have demonstrated that PDOs can maintain the genomic/clonal heterogeneity of tumors, as well as expression heterogeneity. However, the spatial heterogeneity of PDOs and its correlation with the original tumors remains unclear. We propose an integrated pipeline combining spatial transcriptomics and phenotypic analyses of PDOs to capture and track spatial heterogeneity. This platform integrates multiplexed organoid cultures with imaging-based spatial transcriptomics and analysis at single organoid resolution. We validate the platform by comparing results with non-spatial single-cell transcriptomic data and spatial transcriptomic analysis of donor tumors. The combination of ex vivo PDOs and spatial transcriptomics could open novel avenues for developing drugs.

cancer biology↗

bioRxiv2025

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

bioRxiv2025

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC methods applied to 15 datasets spanning 9 technologies and diverse tissue types. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods across tissues and platforms. We also demonstrate that anatomical labels commonly used as ground truths are often biased, potentially error-prone, and, in some cases, unsuitable for benchmarking efforts. Rather than scoring and comparing methods, we propose a consensus-guided workflow that aggregates clustering results to generate consensus representations. Descriptive spatial metrics highlight areas of high entropy where method disagreement is highest, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual methods and manual annotations. Our results underscore the need for iterative, expert-in-the-loop analysis and reveal that traditional evaluation metrics do not always capture the subjective qualities of results. By improving tissue annotation and addressing key benchmarking limitations, SACCELERATOR provides a robust foundation for advancing spatial omics research.

genomics↗

bioRxiv2025

From Transcripts to Cells: Dissecting Sensitivity, Signal Contamination, and Specificity in Xenium Spatial Transcriptomics

Spatial transcriptomics has transformed our ability to map gene expression within intact tissues at cellular and subcellular resolution. Among current platforms, Xenium is widely adopted for its reliability, accessibility, and high data quality. Yet, the properties and limitations of Xenium-derived data remain poorly characterized. Here, we present one of the most comprehensive Xenium datasets to date, encompassing over 40 breast and lung tumor sections profiled using a diverse set of gene panels. Leveraging this resource, we systematically dissect technical noise--including transcript diffusion--alongside assay specificity, panel performance, and segmentation strategies. Our comparison of targeted panels with the newer 5K panel reveals that although the latter captures more transcripts overall, it suffers from reduced per-gene sensitivity and persistent diffusion, even with enhanced chemistry. We demonstrate that single-nucleus RNA-seq (snRNA-seq) markedly improves cell type annotation and enables more precise quantification of diffusion. Building on this, we introduce SPLIT (Spatial Purification of Layered Intracellular Transcripts), a novel method that integrates snRNA-seq with RCTD deconvolution to enhance signal purity. SPLIT effectively resolves mixed transcriptomic signals, improving background correction and cell-type resolution. Together, our findings provide a critical benchmark for Xenium performance and introduce a scalable strategy for signal refinement--advancing the accuracy and utility of spatial transcriptomics.

cancer biology↗

bioRxiv2024

Follicular Bcl6 reactivity is associated with a unique immune landscape and spatial transcriptome in COVID-19

The regulation of follicular (F) and germinal center (GC) immune reactivity in human lymph nodes (LNs), particularly during the early stages of viral infections, remains poorly understood. We have analyzed lung-draining lymph nodes (LD-LNs) from COVID-19 autopsies using multiplex imaging and spatial transcriptomics to examine the immune landscape and with respect to the aging. We identified three subgroups of Reactive Follicles (RFs) based on Bcl6 prevalence, RF-Bcl6no/low, RF-Bcl6int and RF-Bcl6high. RF-Bcl6high tissues express a distinct B/TFH immune landscape associated with increased prevalence of proliferating B- and TFH-cell subsets. Comparison between LD-LNs and matched subdiaphragmatic LNs revealed a disconnected Bcl6 reactivity between the two anatomical sites. LD-LNs Bcl6 reactivity was associated with a distinct spatial transcriptomic profile. TH1-associated genes/pathways (e.g. CXCR3, STAT5, TNF signaling) were significantly upregulated in RF-Bcl6no/low tissues while the RF-Bcl6high tissues exhibited significant upregulation of GC-promoting genes/pathways (e.g. CXCL13, B cell receptor signaling). Despite the similar prevalence, the in-situ transcriptome profiling indicates a higher monocyte/macrophage functionality in "Aged" compared to "Young" follicles from donors with comparable Bcl6 reactivity. Our findings reveal a heterogeneous F/GC landscape in COVID-19 LD-LNs and highlight specific molecular targets and pathways that could regulate human F/GC immune dynamics during early viral infections.

immunology↗

bioRxiv2024

Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity

Recent advancements in probe-based, full-transcriptome, high-resolution technologies for Formalin-Fixed Paraffin-Embedded (FFPE) tissues, such as Visium CytAssist, Chromium Flex (10X Genomics), and GeoMx DSP (Nanostring), have opened new opportunities for studying decades-old archival samples in biobanks, facilitating the generation of data from extensive cohorts. However, the experimental protocols can be labor-intensive and costly; therefore, it is thus essential for researchers to carefully evaluate the strengths and limitations of each technology in relation to their specific research objectives. Here, we report the results of a comparative analysis of the three methods mentioned above on FFPE archival tumor samples from four non-small cell lung cancer, four breast cancer and six diffuse large B-cell lymphoma. We highlight some relative advantages and disadvantages of each method in the context of operational challenges, bioinformatic analysis and biological discovery. Our results show that: 1) all three methods yielded good-quality, highly reproducible transcriptomic data from serial sections of the same FFPE block; 2) GeoMx data contained mixtures of cell types, even when pre-selecting areas with cell type-specific markers; 3) high-throughput spot-level (Visium) or cell-level (Chromium) data enabled the identification of tumor heterogeneity within and between patients, which could be used to identify targeted therapies. Our data support the use of Visium and Chromium for high-throughput and discovery-driven projects, while the GeoMx platform could be suited for addressing specialized questions on targeted regions. All data generated from this study, including GeoMx, Visium, Chromium, H&E, and expert annotations are publicly available.

bioinformatics↗

bioRxiv2024

KIR3DL1 and Tox identify clonally expanded encephalitogenic neuron-specific CD8+ T cells in autoimmune encephalitis

Autoreactive CD8+ T cells are the principal suspects in autoimmune encephalitis (AIE) with antibodies targeting intracellular neuronal antigens So far, the search for neuron-autoreactive CD8+ T cells has been focused on a few autoantigens and did not yield convincing results. Here, we leveraged natural antigen presentation by hiPSC-derived neurons to look at the global autoreactive CD8+ T cell response, independently of pre-conceived hypothesis of the autoantigens involved in the disease. This unbiased approach allowed for the identification of rare polyclonal neuron-reactive CD8+ T cells in healthy donors, and contrastingly, expanded clonotypes in two patients with anti-Ri AIE. Detailed ex vivo phenotypic characterization of these clonotypes revealed a specific transcriptional program suggestive of a pathogenic potential. In particular, this subset can be identified by the expression of KIR3DL1 and TOX. Strikingly, we could also demonstrate that CD8+ T cells found in the brain of an anti-Ri AIE patient display a similar phenotype associated with cytotoxicity and encephalitogenic features.

immunology↗

bioRxiv2023

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

bioinformatics↗

bioRxiv2022

Tumor Microenvironment Cellular Crosstalk Predicts Response to Adoptive TIL Therapy in Melanoma

Adoptive cell therapy (ACT) using ex vivo expanded tumor-infiltrating T lymphocytes (TILs) can mediate responses in metastatic melanoma, but long-term efficacy remains limited to a fraction of patients. Here we interrogated tumor-microenvironment (TME) cellular states and interactions of longitudinal samples from 13 metastatic melanoma patients treated with TIL-ACT in our clinical study (NCT03475134). We performed single-cell RNA-seq and spatial proteomic analyses in pre- and post-ACT tumor tissues and showed that responders exhibited higher tumor cell-intrinsic immunogenicity. Also, endogenous CD8+ TILs and myeloid cells of responders were characterized by increased cytotoxicity, exhaustion and costimulation and type-I IFN signaling, respectively. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders have rich baseline intratumoral and stromal tumor-reactive T-cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study reveals CD8+ T-cell network-based biomarkers that could improve patient selection and guide the design of ACT clinical trials. One-Sentence SummaryResponse to adoptive TIL therapy in melanoma is determined by CD8+ TIL-myeloid cell networks

immunology↗

bioRxiv2022

Single-cell profiling uncovers a Muc4-expressing metaplastic gastric cell type sustained by Helicobacter pylori-specific inflammation

AbstractMechanisms for Helicobacter pylori (Hp)-driven stomach cancer are not fully understood. In a transgenic mouse model of gastric preneoplasia, concomitant Hp infection and induction of constitutively active KRAS (Hp+KRAS+) alters metaplasia phenotypes and elicits greater inflammation than either perturbation alone. Gastric single-cell RNA-seq showed that Hp+KRAS+ mice had a large population of metaplastic pit cells that expressed the intestinal mucin Muc4 and the growth factor amphiregulin. Metaplastic pit cells were associated with macrophage and T cell inflammation and prevented by gastric immunosuppression. Lineage tracing showed that Muc4 was not dependent on cell-intrinsic KRAS activity, and lineage-derived cells had a limited propensity for growth as organoids, demonstrating that metaplastic pit cells are largely not self- renewing. Finally, MUC4 expression was significantly associated with proliferation in human gastric cancer samples. These studies identify an Hp-associated metaplastic pit cell lineage, also found in human gastric cancer tissues, whose expansion is driven by Hp-dependent inflammation. Statement of SignificanceUsing a mouse model, we have delineated metaplastic pit cells as a pre-cancerous cell type whose expansion requires H. pylori-driven inflammation. In humans, metaplastic pit cells show enhanced proliferation as well as enrichment in precancer and early cancer tissues, highlighting an early step in the gastric metaplasia to cancer cascade.

microbiology↗

bioRxiv2022

An information theoretic approach to detecting spatially varying genes

Identifying genes with spatially coherent expression patterns is a key task in spatial transcriptomics. We adopt an information theoretic perspective on this problem by equating the degree of spatial coherence with the mutual information between nearby expression measurements. To avoid the notoriously difficult problem of computing mutual information, we use modern methods of approximation, in a method we call maximization of spatial information (Maxspin). As well as being highly scalable, we demonstrate improved accuracy across several spatial transcriptomics platforms and a variety of simulations when compared to both existing specialized methods and traditional spatial statistics methods. We use the method to analyze a renal cell carcinoma sample profiled using CosMx Spatial Molecular Imaging, revealing previously undescribed gene expression patterns.

bioinformatics↗

bioRxiv2022

Robust Normalization and Integration of Single-cell Protein Expression across CITE-seq Datasets

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

bioinformatics↗

bioRxiv2022

Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses

Systems biology approaches have been used to define molecular signatures and mechanisms of immunity to vaccination. However, most such studies have been done with single vaccines, and comparative analysis of the response to different vaccines is lacking. We integrated temporal transcriptional data of over 3,000 samples, obtained from 820 healthy adults across 28 studies of 13 different vaccines and analyzed vaccination-induced signatures associated with the antibody response. Most vaccines induced similar kinetics of shared transcriptional signatures, including signatures of innate immunity occurring 1-3 days post-vaccination, as well as the canonical plasmablast and cell cycle signatures appearing 7 days post-vaccination. However, the yellow fever vaccine YF-17D uniquely induced an early transient signature of T and B cell activation at Day 1, followed by delayed antiviral/interferon and plasmablast signatures that peaked at Days 7 and 14-21, respectively. Thus, despite the shared transcriptional response to most vaccines, at any given time point there was no evidence for a "universal signature" that could be used to predict the antibody response to all vaccines. However, accounting for the asynchronous nature of responses led to the identification of a time-adjusted signature that improved prediction antibody of responses across vaccines. These results provide a transcriptional atlas of the human immune response to vaccination and define a common, time-adjusted signature of antibody responses to vaccination.

immunology↗

bioRxiv2022

Defining cellular population dynamics at single cell resolution during prostate cancer progression

Advanced prostate malignancies are a leading cause of cancer-related deaths in men, in large part due to our incomplete understanding of cellular drivers of disease progression. We investigated prostate cancer cell dynamics at single-cell resolution from disease onset to the development of androgen independence in vivo. We observe a dramatic expansion of a castration-resistant intermediate luminal cell type that correlates with treatment resistance and poor prognosis in human patients. Moreover, transformed epithelial cells and associated fibroblasts create a microenvironment conducive to pro-tumorigenic immune infiltration, which is in part androgen responsive. Androgen independent prostate cancer leads to significant diversification of intermediate luminal cell populations characterized by a range of androgen signaling activity inversely correlated with proliferation and mRNA translation. Accordingly, distinct epithelial populations are exquisitely sensitive to translation inhibition which leads to epithelial cell death, loss of pro-tumorigenic signaling, and decreased tumor heterogeneity. Our findings reveal a complex tumor environment largely dominated by castration-resistant luminal cells and immunosuppressive infiltrates.

cancer biology↗

bioRxiv2021

The Immune Signatures Data Resource: A compendium of systems vaccinology datasets

Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines, including those targeting seasonal influenza, yellow fever, and hepatitis B. These data are made available to the broader scientific community through the ImmuneSpace data portal and analysis engine leveraging the NIH/NIAID ImmPort repository1,2. However, a barrier to progress in this area is that comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines and includes transcriptional profiles and antibody response measurements. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses. [Table 1]

systems biology↗

bioRxiv2021

Global and context-specific transcriptional consequences of oncogenic Fbw7 mutations

Fbw7 is a ubiquitin ligase substrate receptor that targets proteins for proteasomal degradation. Most known Fbw7 substrates are transcription factors (TFs) and many are also oncoproteins (e.g., c-Myc, c-Jun, Notch). Fbw7 is an important tumor suppressor and FBXW7 mutations drive tumorigenesis through activation of oncogenic Fbw7 substrates. Defining the mechanisms of Fbw7-associated tumorigenesis is critical for developing targeted therapies. We thus determined the transcriptional consequences of oncogenic Fbw7 mutations by studying isogenic colorectal cancer cell lines with engineered FBXW7 null and heterozygous missense mutations. We used an integrated approach employing RNA-Seq and high-resolution mapping (CUT&RUN) of histone modifications and TF occupancy (c-Jun and c-Myc) to examine the combinatorial effects of mis-regulated Fbw7 substrates. Fbw7 mutations caused widespread transcriptional changes associated with active chromatin and altered TF occupancy at distal regulatory regions. Some regulatory changes were common to both FBXW7-mutant cell lines whereas others were FBXW7 mutation-specific. By comparing c-Jun and c-Myc binding sites, we also identified co-regulated elements, suggesting that Fbw7 substrates may have synergistic effects. One co-regulated gene was CIITA, a master regulator of MHC Class II gene expression, and Fbw7 loss increased CIITA and MHC Class II gene expression in colorectal cancer cells. Fbw7 mutations were also correlated with increased CIITA expression in TCGA colorectal tumors and cell lines, which may have immunologic implications for progression and treatment of Fbw7-associated cancers. This integrative analysis provides a framework for understanding normal and neoplastic context-specific Fbw7 functions.

cancer biology↗