Biology subjects

Bosisio, F. M.

Publications and source records attributed to Bosisio, F. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Mapping tumor microenvironment heterogeneity of human melanoma metastases across distant organs

Distant metastasis has a major impact on melanoma mortality, yet it remains poorly defined how disseminated cells adapt to different host organs and their microenvironment. Here we applied single-nucleus RNA sequencing (snPATHO-seq) to archival FFPE melanoma metastases spanning brain, liver and lung from different patients. We built an atlas resolving malignant, immune and stromal compartments in each organ. Within the malignant compartment, we observed a differentiation axis of melanocytic, transitory, neural crest-like and neural crest-like/mesenchymal states; and meta-programs: interferon-responsive, hypoxic, cycling and stress. The immune compartment showed organ specificity: resident macrophage identity recapitulated the host tissue (microglia, Kupffer cells and alveolar macrophages). Brain-infiltrating myeloid and lymphoid cells were transcriptionally the most immunosuppressive and most cytotoxic, respectively, of the three sites. Non-malignant stromal populations, including fibroblasts, endothelial cells and pericytes, also carried distinct organ-specific transcriptional programs, suggesting that organ-of-residence effects extend beyond the malignant cells to the whole metastatic ecosystem. We provide a first multi-organ atlas of melanoma distant metastasis allowing deeper understanding of how a melanoma is remodelled by, and/or remodels, three distinct human organ environments.

cancer biology↗

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

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↗

bioRxiv2025

The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomic.

Lymph nodes (LN) are key secondary lymphoid organs (SLO) for a coordinated immune response. They have been extensively characterized by numerous investigative techniques chiefly as single cell suspensions because they are composed of vagile yet crowded hematolymphoid elements, unfriendly to spatial tissue organization-saving techniques. We comprehensively classify in situ all cells of 19 human LN free of pathology with a 78-marker antibody panel, an hyperplexed cyclic staining method, MILAN, and an analytical bioinformatic pipeline, BRAQUE. A total of 77 cell types were classified, encompassing T, B, innate immune and stromal cells. CD4 and CD8 T-cells were classified into 27 unique subsets by leveraging the expression profiles of TCF7, the presence of co-inhibitory receptors and the spatial distribution. CD5 and TCF7 expression defined novel B-cell types. CD27+ mature B-cells occupied previously unrecognized nodal spaces non-overlapping with the cortex and the plasma-cell rich medullary cords. Type 2 conventional dendritic cells were located in nodular paracortical aggregates. Statistically controlled pairwise neighborhood analysis showed sparse cell-cell interactions, known and new neighbors, established and novel LN landscape niches. A high-dimensional proteomic interrogation of the normal human LN provides spatial allocation of known cell types, novel interactions and the landscape organization.

immunology↗

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

An autophagy program that promotes T cell egress from the lymph node controls responses to immune checkpoint blockade

Lymphatic endothelial cells (LECs) lining the lymphatic vessels of the lymph node (LN) parenchyma orchestrate leukocyte trafficking and peripheral T cell dynamics. T cell responses to immunotherapy largely rely on peripheral T cell recruitment in tumors. Yet, a systematic and molecular understanding of how LECs within the LNs control T cell dynamics under steady state and tumor-bearing conditions is lacking. Using intravital and high-resolution imaging combined with immune phenotyping, we show that LEC-specific deletion of the essential autophagy gene Atg5 alters intranodal positioning of lymphocytes and accrues their persistence in the LNs, by increasing the availability of the main egress signal S1P. Single-cell RNA-sequencing of tumor-draining LNs from WT and ATG5LEC-KO mice unveils that loss of ATG5 remodels niche-specific LEC phenotypes, involved in molecular pathways regulating lymphocyte trafficking and LEC-T cell interactions. Functionally, loss of LEC-autophagy prevents recruitment of tumor-infiltrating T cells and NK cells and abrogates tumor regression in response to anti-PD-1 or anti-CTLA4-based immunotherapy. Thus, a unique LEC-autophagy program boosts immune-checkpoint responses by guiding systemic T cell dynamics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/549282v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1a4bfe1org.highwire.dtl.DTLVardef@1317fe2org.highwire.dtl.DTLVardef@c2fa20org.highwire.dtl.DTLVardef@b35dba_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

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↗

bioRxiv2021

Anticancer immunotherapies transition postcapillary venules into high-endothelial venules that generate TCF1+ T lymphocyte niches through a feed-forward loop

The lack of T-cell infiltrates is a major obstacle to effective immunotherapy in cancer. Conversely, the formation of tumor-associated tertiary-lymphoid-like structures (TA-TLS), which are the local site of humoral and cellular immune responses against cancers, are associated with good prognosis and have recently been detected in Immune Checkpoint Blockade (ICB)-responding patients. However, how these lymphoid aggregates develop remains poorly understood. By employing scRNA sequencing, endothelial fate mapping, and functional multiplex immune profiling, we demonstrate that antiangiogenic immune-modulating therapies evoke the transition of postcapillary venules into inflamed high endothelial venules (HEVs), which generate permissive TA-TLS-like lymphocyte niches with PD1neg and PD1+TCF1+CD8 T cell progenitors that differentiate into GrzB+TCF1neg TIM3+ PD1+ CD8 T effector cells. Tumor-HEVs require continuous CD8 and NK cell-derived lymphotoxin signals revealing that tumor-HEV maintenance is actively sculpted by the adaptive immune system through a feed-forward loop. In BriefHua & Vella et al. reveal that effective antiangiogenic immunotherapy transitions postcapillary venules into inflamed high-endothelial venules (HEV), sustained by CD8 T and NK cell-derived signals through a feed-forward loop. Thereby, tumoral HEVs establish perivascular niches in which TCF1+ PD1+ lymphocytes expand and produce cytolytic PD1+ TIM3+ CD8 T cells that facilitate anti-tumoral immunity. HighlightsO_LIHigh endothelial venule induction by anticancer immunotherapies generates perivascular immune niches permissive for TCF1+ PD1+ CD8 progenitor T cell expansion and production of TCF1neg PD1+ TIM3+ CD8 effector T cells C_LIO_LITumoral high-endothelial venules exhibit characteristics of inflamed lymph node HEVs and postcapillary venules C_LIO_LIPostcapillary venules dynamically transdifferentiate into high-endothelial venules in tumors, which requires continuous signals from surrounding immune cells C_LIO_LICD8 and NK cells drive tumoral high-endothelial venule formation during antiangiogenic immunotherapies in a feed-forward loop via lymphotoxin beta receptor signaling C_LI

cancer biology↗

bioRxiv2021

Immunogenomic, single-cell and spatial dissection of CD8+T cell exhaustion reveals critical determinants of cancer immunotherapy

Tumoural-CD8+T cells exhibit exhausted or dysfunctional states. Contrary to immunotherapy-responsive exhausted-CD8+T cells, the clinical features of dysfunctional-CD8+T cells are disputed. Hence, we conducted large-scale multi-omics and multi-dimensional mapping of CD8+T cell-states across multiple cancer patient-cohorts. This identified tumour-specific continuum of CD8+T cell-states across 6 human cancers, partly imprinted by organ-specific immuno-modulatory niches. Herein, melanoma and glioblastoma enriched prototypical exhausted (CD8+TEXT) and severely-dysfunctional (CD8+TSDF) states, respectively. Contrary to CD8+TEXT, CD8+TSDF displayed transcriptomic and epigenetic effector/cytolytic dysfunctions, and dysregulated effector/memory single-cell trajectories, culminating into maladaptive prodeath stress and cell-cycle defects. Suboptimal antigen-priming underscored CD8+TSDF, which was distinct from immune-checkpoints "rich" CD8+TEXT, reflecting chronic antigen-stimulation. Continuum variation also existed on tumour spatial-level, with convergent (CD8+TEXT-supportive vascular regions) and divergent features (dysfunctional CD4+T::CD8+TSDFcell-to-cell interactions) between melanoma and glioblastoma. Globally, IFN{gamma}-IL2 disparities, paucity of intra-tumoural CD4+/CD8+T cells, and myeloid TGF{beta}/wound healing responses, distinguished CD8+TSDF-landscape. Within immuno-oncology clinical-trials, anti-PD1 immunotherapy failed to "reinvigorate" CD8+TSDF-landscape, and instead facilitated effector-dysfunction and TGF{beta}/wound healing. However, cellular immunotherapies (dendritic cell-vaccines, adoptive T-cell therapy) ameliorated assorted CD8+TSDF-landscape disparities, highlighting a roadmap for anti-glioblastoma multimodal-immunotherapy. Collectively, our study comprehensively expands clinical-knowledge on CD8+T cell-exhaustion and suggests that tumour-specific, pre-existing CD8+TEXT/TSDF-states, determine immunotherapy-responses.

immunology↗

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

Activation of the Integrated Stress Response in drug-tolerant melanoma cells confers vulnerability to mitoribosome-targeting antibiotics.

Therapy resistance remains a major clinical challenge for the management of metastatic melanoma. Here we show that activation of the Integrated Stress Response (ISR), which we show is common in drug-tolerant and resistant melanoma, promotes selective synthesis of mitochondrial proteins in the cytosol. Since mitochondrial translation adapts to the influx of nuclear-encoded mitochondrial proteins, ISR activation indirectly enhances mitochondrial translation and makes these cells highly vulnerable to mitochondrial translation inhibitors. Treatment of melanoma with mitoribosome-targeting antibiotics, induces proteotoxic stress and significantly compromises the growth of NRAS-mutant and immunotherapy-resistant skin melanoma as well as uveal melanoma. Additionally, a triple BRAFi/MEKi/Tigecycline combination reduces intratumour heterogeneity by abrogating emergence of dedifferentiated drug-tolerant cells, and delayed or even prevented the development of resistance in BRAFV600E PDX models. Consistently, a melanoma patient exposed to Doxycycline, a mitoribosome-targeting antibiotic commonly used to treat infections, experienced a complete and long-lasting response of a treatment-resistant lesion. SignificanceOur study indicates that the repurposing of mitoribosome-targeting antibiotics offers a rational salvage strategy for targeted therapy in BRAF-mutant melanoma, and a therapeutic option to target NRAS-driven and immunotherapy-resistant cutaneous melanoma and uveal melanomas.

cancer biology↗

bioRxiv2020

NOT ALL ANTIBODIES HAVE BEEN CREATED EQUAL: Antibodies validated for routinely processed tissue seldom apply to frozen tissue sections.

ABSTRACTA customized context-dependent validation of antibodies for the prospective use, rather than a general stamp of validity, is required for reproducibility and data validity, besides the need of standardized reagents. In-situ antibody staining is a technique broadly used in experimental settings and human diagnostic practice. The first typically, but not exclusively uses lightly fixed material (cell smears, frozen sections), the second, routinely processed formalin-fixed, paraffin-embedded (FFPE) tissue. Differently from techniques based on tissue extraction, there is little awareness of the context-dependent constraints inherent with either type of in situ staining except that antigen masking associated with routine tissue processing limits the range of useful antibodies. We applied a panel of 126 antibodies validated for FFPE to lightly fixed (acetone, formalin) frozen sections and found that less than 30% performed conservatively with all fixations, 35% preferred one fixation over another, 13% gave non-specific staining, 23% did not stain at all. Individual antibody variegation of the paratope fitness for the differentially fixed antigen may be the cause. Re-validation of established antibody panels is required when applied to sections whose fixation and processing is different from the tissue where they have been initially validated.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pathology↗

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

The landscape of S100B+ and HLA-DR+ dendritic cell subsets in tonsils at the single cell level via high-parameter mapping

Dendritic cells (DC) (classic, plasmacytoid, inflammatory) are an intense focus of interest because of their role in inflammation, autoimmunity, vaccination and cancer. We present a tissue-based classification of human DC subsets in tonsils with a high-parameter (>40 markers) immunofluorescent approach, cell type-specific image segmentation and the use of bioinformatics platforms. Through this deep phenotypic and spatial examination, classic cDC1, cDC2, pDC subsets have been further refined and a novel subset of DC co-expressing IRF4 and IRF8 identified. Based on unique tissue locations within the tonsil, and close interactions with T cells (cDC1) or B cells (cDC2), DC subsets can be further subdivided by correlative phenotypic changes associated with these interactions. In addition, monocytes and macrophages expressing HLA-DR or S100AB are identified and localized in the tissue. This study thus provides a whole tissue in situ catalog of human DC subsets and their cellular interactions within spatially defined niches.

immunology↗