Similar Datasets
Project description:DSP RNA profiling was performed on a cohort of immunotherapy treated NSCLC patients
2023-12-20 | GSE221733 | GEO
Project description:Nanostring DSP was performed on 25 TMA cores from TNBC patients with separate tumour and stroma compartment samples De proteins were identified in tumour and stromal compartments
2022-11-29 | GSE218973 | GEO
Project description:GeoMx Digital Spatial Profiling (Nanostring) is a commercial spatial transcriptomics method to selectively analyze regions of interest within intact tissue sections. However, it is unclear how tissue decalcification affects the results of this assay, or if decalicified samples are comparable to untreated samples. For this purpose, we assembled a set of tissues that were subjected to progressive durations of incubation with ethylene-diamine-tetra-acetic (EDTA), and analyzed comparable regions of the tissue using GeoMx DSP. In the associated manuscript, we show that EDTA incubation attenuates signals, and distorts counts from a subset of probes. We determine the undisclosed full-length target sequences of probes used in the human whole transcriptome panel, updating target transcripts and genes, and identify that gene-promiscuous probes are particularly affected by normalization after EDTA incubation. For the purpose of this study, we prepared series of non-calcified tissues that were incubated with EDTA for increasing time periods, and matched control samples from the same source tissue, which were left untreated. Specimens submitted for routine tissue analysis were collected for research purposes after approval of the ethics commission of the Medical University of Vienna (2167/2021, registered at https://ekmeduniwien.at/core/catalog/2022/) as graphically shown in Figure 1A). In the course of routine pathology workup, 24 clinical tissue specimens (Table S1) of different organs stemming from 20 patients were fixed in 7.5% buffered formalin for 2.0 to 3.5 (mean 3.3, median 3.0) days according to the laboratory’s routine procedure. At macroscopic dissection, an excess piece of tissue was removed and divided into 4 macroscopically similar samples. Tissues were then either processed immediately in a Tissue-Tek VIP® 6 AI (Sakura Finetek) or incubated at room temperature in EDTA solution (Tritiplex, Glatt-Koller, 403212270) for 1, 3, and 7 days before processing them analogous to the untreated tissue. Samples were embedded in paraffin blocks according to standard protocols using low-melting point Paraffin (Histo-Comp, ATS-200856). Blocks were sectioned, stained with hematoxylin/eosin and scanned (Pannoramic 250 Flash II, 3DHistech) to confirm the content of equivalent histological structures among samples sourced from the same specimen. Areas for tissue microarray (TMA) cores were selected using the digital slide scans. Tissue microarrays were then assembled using an automated TMA device (TMA Grandmaster, 3DHistech) using 1mm punches and a recipient block of 5mm depth. TMA were tempered by alternating storage at 37°C, 4°C, and room temperature, for 5 times (2h each), following by a single incubation at 65°C for 10 minutes. Slides were sectioned using a rotating microtome at 5µm thickness. One section was used for DSP analysis as outlined below, while an adjacent section was used for validation of the TMA cores using hematoxylin & eosin stain. DSP was performed according to protocols provided by Nanostring using the following standard parameters. In brief, sections were deparaffinized and subjected to heat-induced protein epitope retrieval (using retrieval buffer at pH 9, Thermo Fisher Scientific, 00-4956-58) for 15 minutes at 100°C using a steam cooker. Retrieval for mRNA was performed using proteinase K (Thermo Fisher Scientific, 25530049) at 1µg/ml for 15 minutes. Slides were then incubated with the human Whole Transcriptome Atlas (WTA) probe panel (Nanostring, NA-GMX-RNA-NGS-HuWTA-4, Lot HWTA21003) at 37°C in a humidified environment overnight. Sections were washed according to standard protocols and direct immunofluorescence was performed with antibodies against CD34 conjugated to Alexa-647 (Novus Biologicals, clone QBEnd-10, NBP2-34713AF647), Pan-cytokeratin conjugated to to Alexa532 (Novus Biologicals, clone AE1/AE3, NBP2-33200), CD45 conjugated to Alexa594 (clone 2B11 + PD7/26, Novus Biologicals, NBP2-34528), and a nuclear counterstain (Syto-13, Nanostring) DSP device run and library preparation were performed according to the manufacturers’ protocol. The library was sequenced using an Illumina NextSeq 550 sequencer with the specifications provided by Nanostring (paired-end reads at length 27, index length 8) at a library concentration of 1.6 pM using a NextSeq 550 High Output 75-cycle kit (Illumina, 20024906). Nuclei counts of regions of interest were obtained via the default automatic nuclear segmentation settings of the DSP device software.
2024-09-18 | GSE272995 | GEO
Project description:Purpose: Digital spatial profiling was used to elucidate the interplay between the tumor cells and the surrounding microenvironment FFPE tissues. Methods: Two tissue microarrays (TMA) were generated from each FFPE block using the ISNET Galileo CK4500 computer-driven tissue microarray. Images of H&E-stained cores were demarcated by a pathologist and were utilized alongside NanoString immunofluorescent staining for morphology markers S100, CD45, CD3, and DAPI to draw region of interest.
2022-04-16 | GSE200904 | GEO
Project description:Nanostring DSP was performed on 187 ROIS from 17 HNSCC patients and differential analysis was performed by patient response groups to immunotherapy. DE proteins were identified in both tumour and stromal tissue compartments
2022-11-14 | GSE217640 | GEO
Project description:DSP protein profiling using the nanostring immuno-oncology kit was performed on head and neck tumor tissue samples De proteins were identified in tumour and stromal compartments
2024-08-02 | GSE259280 | GEO
Project description:Cancer-associated fibroblasts (CAFs) suppress anti-tumor immunity mediated by T-lymphocytes – making CAFs tempting targets for enhancing cancer immunotherapy. Unfortunately, the signaling mechanisms driving CAF activity in tumors have crucial functions outside tumors. Consequently, agents like angiotensin receptor blockers (ARBs) that inhibit CAF activity are limited by systemic adverse effects such as hypotension. To address this challenge, we developed tumor microenvironment-activated ARBs (TMA-ARBs) by chemically linking ARBs to novel polymeric materials that selectively degrade in the tumor microenvironment. TMA-ARBs remain in an inactive, material-bound state until they reach tumors, wherein the ARBs become active as the materials break apart. This tumor-selective activity enhances the CAF-inhibiting effects of ARBs while eliminating blood pressure-lowering effects. By reducing CAF activity, TMA-ARBs induce a comprehensive immunostimulatory shift in the tumor microenvironment with improved T-lymphocyte activity and distribution. Accordingly, TMA-ARBs dramatically increase animal survival when combined with immunotherapy in mice with metastatic breast cancer.
2019-10-01 | GSE98827 | GEO
Project description:Background: A pressing challenge in treating non-small cell lung cancer (NSCLC) lies in the significant variability of patient responses to immunotherapy. This issue underscores the critical need for innovative predictive models that can navigate beyond conventional biomarkers to enhance patient outcomes, particularly by addressing immunotherapy resistance or responsiveness. Hypothesis: We hypothesize that an integrated multi-omics approach will uncover interactions within the NSCLC tumor immune microenvironment (TIME) and identify novel biomarkers that are predictive of individual immunotherapy responses, thus aiding in the development of a robust personalized treatment planning model. Objective: To develop a predictive model for NSCLC immunotherapy response/resistance by identifying new biomarkers using co-detection by indexing (CODEX) and Digital Spatial Profiling of whole transcriptome atlas (DSP-WTA). Methods: We utilized a multi-omics approach, combining CODEX for spatial mapping of protein expression at the single-cell level and DSP-WTA for comprehensive transcriptomic insights from the cell types identified by CODEX. This methodology facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first-line immunotherapy. Results: Our analysis identified three cell types, proliferating tumor cells, granulocytes, and vessels, that are associated with resistance to immunotherapy. The high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the training cohort (N = 33) and 1.8 (p = 0.05) in the validation cohort (N = 35). In the response cell type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells returned a HR of 0.4 (p = 0.019) in the training set and 0.49 (p = 0.036) in the validation set. Gene signatures related to these cell types also predicted outcomes with high accuracy. The resistant gene model, which included 8 genes associated with epithelial-mesenchymal transition (EMT) and cell migration showed a HR of 5.3 (p < 0.001) in the training set and 2.2 (p = 0.036) in the validation set. The response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the training set and 0.38 (p = 0.034) in the validation set. Conclusion: This research highlights the potential of a multi-omics strategy in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.
2024-08-22 | GSE271689 | GEO
Project description:34 NSCLC cell lines were transcriptionally profiled against a reference mix of 45 NSCLC cell lines to look subtype specific differential gene expression 34 individual cell lines were compared to a reference mix consiting of 45 NSCLC cell lines
2013-01-18 | E-GEOD-43567 | biostudies-arrayexpress
Project description:Dicer knockout NSCLC mRNAseq profiles the transcriptome, Dicer knockout NSCLC miRseq profiles the miRnome DicerHet and DicerKO NSCLC, 2 biological reps each genotype for mRNAseq, 1 biological rep each for miRseq
2014-04-25 | E-GEOD-57043 | biostudies-arrayexpress