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Biology subjects

Braubach, O.

Publications and source records attributed to Braubach, O..

9 recordsLinked to original sources

Spatial multi-omics identify an immunosuppressive lipid-laden macrophage niche in primary CNS lymphoma

Primary central nervous system lymphoma (PCNSL) is histologically a subtype of diffuse large B-cell lymphoma (DLBCL), sharing genetic and transcriptomic similarity, but with distinct clinical features, particularly its confinement to the CNS and higher relapse risk. The microenvironmental basis for its divergence from systemic DLBCL remains unclear. Using spatial transcriptomic approaches (Xenium/GeoMx digital spatial profiling) in a comparative study of PCNSL (n=17) and DLBCL (n=76), we found that PCNSL, unlike systemic DLBCL, is dominated by an immunosuppressive macrophage compartment enriched for cholesterol-metabolism programs. In independent cohorts of PCNSL profiled by single-cell RNA sequencing, we validated the presence of a recurrent population of lipid-laden macrophages (LLMs): TREM2/GPNMB-expressing, lipid-remodeled cells transcriptionally distinct from resident microglia and consistent with an infiltrating monocyte origin, not previously characterized in CNS lymphoma. LLMs formed immunosuppressive niches with regulatory T cells, and using Cellscape hyperplex proteomic imaging we demonstrate that LLM-Treg spatial interactions are associated with chemotherapy response. To test whether LLMs are lymphoma-driven and functionally important, we developed an immunocompetent syngeneic PCNSL mouse model, driven by Myd88L252P and Cd79b mutations with Bcl2 overexpression. Monocyte-derived macrophages in lymphoma-bearing brain regions acquired an LLM-like state, not seen in lymphoma-free brain regions or in splenic tumors driven by the same oncogenic lesions. TREM2-SYK signaling sustained this state, and SYK inhibition reversed its tumor-supportive activity ex vivo. These findings identify the LLM program as a targetable immunosuppressive myeloid state in CNS lymphoma.

cancer biology↗

Spatial Multi-Omics Workflow and Analytical Guidelines for Alzheimer's Neuropathology

Spatial biology technologies enable high-dimensional profiling within intact tissues, revealing how molecular and cellular organization drives function and disease. As these platforms gain broader adoption, standardized analytical frameworks are needed to ensure data quality and reproducibility. Here, we present an end-to-end pipeline for the GeoMx Digital Spatial Profiler that simultaneously generates whole-transcriptome and 637-protein measurements from user-defined regions within the same tissue sections. The workflow integrates morphology-guided region selection, quality control, normalization, and multi-modal data interpretation. Applied to formalin-fixed cortical tissues from Alzheimers disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, and controls, the framework resolves spatially distinct molecular domains. Transcript and protein signals diverge across amyloid plaque cores and surrounding glial-rich regions, with RNA-protein concordance varying by disease condition, while single-neuron profiling with and without pathogenic tau deposition illustrates protein assay sensitivity. This dataset provides a rigorously validated resource for spatial multi-omic analyses and establishes broadly applicable guidelines for reliable, reproducible profiling of complex tissues.

neuroscience↗

CellMAPS: A no-code model-based customisable multiplex image analysis workflow

Although multiplex imaging allows simultaneous mapping of complex tissue architectures and cellular phenotypes, the dearth of user-friendly robust image analysis workflows remains a significant limitation to its widespread use, restricting multiplex imaging to laboratories with image analysis expertise. Here, we introduce a no-code model-based customisable suite, CellMAPS, which allows both image processing and spatial analyses by non-specialists. We also developed new tools for tissue de-arraying, image normalisation and cell segmentation to increase usability and reduce analysis subjectivity. We have also introduce new spatial analysis tools for the partition of tissue architectures and cellular microenvironments. We demonstrate the capabilities of CellMAPS in various diseases captured using different multiplex imaging platforms. Overall, CellMAPS provides a platform for inexperienced laboratories to implement multiplex imaging and complex spatial analyses, which ultimately should lead to the broader adoption of these technologies in the biomedical field.

bioinformatics↗

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

systems biology↗

Single-cell Spatial Metabolic and Immune Phenotyping of Head and Neck Cancer Tissues Identifies Tissue Signatures of Response and Resistance to Immunotherapy

Head and neck squamous cell carcinomas (HNSCC) are the seventh most common cancer and represent a global health burden. Immune checkpoint inhibitors (ICIs) have shown promise in treating recurrent/metastatic cases, with durable benefit in [~]30% of patients. Current biomarkers for head and neck tumors are limited in their dynamic ability to capture tumor microenvironment (TME) features, with an increasing need for deeper tissue characterization. Therefore, new biomarkers are needed to accurately stratify patients and predict responses to therapy. Here, we have optimized and applied an ultra-high plex, single-cell spatial protein analysis in HNSCC. Tissues were simultaneously analyzed with a panel of 101 antibodies that targeted biomarkers related to tumor immune, metabolic and stress microenvironments. Our data uncovered a high degree of intra-tumoral heterogeneity intrinsic to head and neck tumors and provided unique insights into the biology of the tumor. In particular, a cellular neighborhood analysis revealed the presence of 6 unique spatial tumor-immune neighborhoods enriched in functionally specialized immune cell subsets across the patient tissue. Additionally, functional phenotyping based on key metabolic and stress markers identified four distinct tumor regions with differential protein signatures. One tumor region was marked by infiltration of CD8+ cytotoxic T cells and overexpression of BAK, a proapoptotic regulator, suggesting strong immune activation and stress. Another adjacent region within the same tumor had high expression of G6PD and MMP9, known drivers of tumor resistance and invasion respectively. This dichotomy of immune activation-induced death and tumor progression in the same sample demonstrates the heterogenous niches and competing microenvironments that underpin clinical responses of therapeutic resistance. Our data integrate single-cell ultra-high plex spatial information with the functional state of the tumor microenvironment to provide insights into a partial response to immune checkpoint inhibitor therapy in HNSCC. We believe that the approach outlined in this study will pave the way towards a new understanding of TME features associated with response and sensitivity to ICI therapies.

cancer biology↗

A Machine Learning One-Class Logistic Regression Model to Predict Stemness in Single Cell Transcriptomics and Spatial Omics Datasets

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches are often biased and manually curated. Here we harness an existing stemness model for assessing oncogenic states to transform its application to single cell and spatial omic datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic or proteomic features from non-transformed stem cells to identify dedifferentiated cell states. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. Finally these stemness indices are applicable across a variety of spatial transcriptomic and proteomic technologies for the identification of oncogenic cell types in the tumor microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/539461v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@f2973forg.highwire.dtl.DTLVardef@a7aa9corg.highwire.dtl.DTLVardef@1b1ea27org.highwire.dtl.DTLVardef@183b4a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

A spatially resolved single cell genomic atlas of the adult human breast

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

genomics↗

Multiplex imaging of human induced pluripotent stem cell-derived neurons with CO-Detection by indEXing (CODEX) technology

BackgroundHuman induced pluripotent stem cell (iPSC) models have been hailed as a breakthrough for understanding disease and developing new therapeutics. The major advantage of iPSC-derived neurons is that they carry the genetic background of the donor, and as such could be more predictive for clinical translation. However, the development of these cell models is time-consuming and expensive and it is thus critical to maximize biomarker readout from every model that is developed. One option is to use a highly multiplexed biomarker imaging assay, like CO-Detection by indEXing (CODEX), which allows detection of 50+ targets in situ at single-cell resolution. New MethodThis paper describes the development of CODEX in neuronal cell cultures derived from human iPSCs. ResultsWe differentiated human iPSCs into mixed neuronal and glial cultures on glass coverslips. We then developed and optimized a panel of 21 antibodies to phenotype iPSC-derived neuronal subtypes of cortical, dopaminergic, and striatal neurons, as well as astrocytes, and pre-and postsynaptic proteins. Comparison with existing methodsCompared to standard immunocytochemistry, CODEX oligoconjugated fluorophores circumvent antibody host interactions and allow for highly customized multiplexing. ConclusionWe show that CODEX can be applied to iPSC neuronal cultures and developed fixation and staining protocols for the neurons to sustain the multiple wash-stain cycles of the technology. Furthermore, we demonstrate both cellular and subcellular resolution imaging of multiplexed biomarkers in the same samples. CODEX is a powerful technique that complements other single-cell omics technologies for in-depth phenotype analysis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/479039v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@68d49dorg.highwire.dtl.DTLVardef@5ea424org.highwire.dtl.DTLVardef@16a763forg.highwire.dtl.DTLVardef@7950fc_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOC_FLOATNO CODEX(R) Multiplex Imaging in human iPSC neurons [A-D] Schematic drawings of the tools and steps used for Co-Detection by indEXing (CODEX) imaging. [A] Target-specific antibodies are conjugated to unique DNA oligonucleotide barcodes. Fluorescent reporter (excitation wavelength at 488 nm, 550 nm, or 647 nm/Cy5) carrying the complementary DNA (to the barcode) enables barcode-specific binding of the reporter to the antibody and detection by fluorescence microscopy. [B] Neuronal cell cultures are prepared for the CODEX staining and imaging by several fixation steps with different PFA concentrations followed by incubation with 100% acetone. Residual acetone is removed by drying the sample. After rehydration with PBS, autofluorescence is quenched by exposure to broad-spectrum LED light. Following a pre-staining fixation step, the sample is incubated with a mix of all conjugated primary antibodies. Excessive, unbound antibodies are removed by a washing step, leaving only the bound antibodies followed by a final post-staining fixation. [C] The CODEX Instrument Manager performs the multicycle run and controls the microscope software for automated addition of reporters, imaging, and washing of the samples (pre-stained with primary antibodies) to remove reporters from each cycle. After imaging, bound reporters are removed without damaging the tissue using a solvent, and the next set of reporters (conjugated to different barcodes) are added. [D] CODEX(R) Processor processes raw files and performs stitching, deconvolution, background subtraction, and cell segmentation. The processed images can be viewed and analyzed with the CODEX(R) Multiplex Analysis Viewer (MAV) plugin using Fiji software. C_FIG

neuroscience↗

Nano immunoconjugates crossing blood-brain barrier activate local brain tumor immune system for glioma treatment

Treatment of brain gliomas with checkpoint inhibitor antibodies to cytotoxic T-lymphocyte-associated antigen 4 (a-CTLA-4) and programmed cell death-1 (a-PD-1) was largely unsuccessful due to their inability to cross the blood-brain barrier (BBB). We describe a new generation of nano immunoconjugates (NICs) developed on natural biopolymer scaffold, poly({beta}-L-malic acid), with covalently attached a-CTLA-4 and/or a-PD-1 for delivery across the BBB and activation of local brain anti-tumor immune response in glioma-bearing mice. NIC treatment of mice bearing intracranial GL261 glioblastoma (GBM) resulted in an increase of CD8+ T-cells with a decrease of T regulatory cells (Tregs) in the brain tumor area. Survival of GBM-bearing mice treated with combination of NICs was significantly longer compared to animals treated by single checkpoint inhibitor-bearing NICs or free a-CTLA-4 and a-PD-1. Our study demonstrates trans-BBB delivery of nanopolymer-conjugated checkpoint inhibitors as an effective treatment of GBM via activation of both systemic and local brain tumor immune response.

immunology↗