To see the other types of publications on this topic, follow the link: Spatial omic.
Journal articles on the topic 'Spatial omic'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Spatial omic.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Xu, Tinghui, and Kris Sankaran. "Interactive visualization of spatial omics neighborhoods." F1000Research 11 (July 18, 2022): 799. http://dx.doi.org/10.12688/f1000research.122113.1.
Full textAbstract:
Dimensionality reduction of spatial omic data can reveal shared, spatially structured patterns of expression across a collection of genomic features. We studied strategies for discovering and interactively visualizing low-dimensional structure in spatial omic data based on the construction of neighborhood features. We designed quantile and network-based spatial features that result in spatially consistent embeddings. A simulation compares embeddings made with and without neighborhood-based featurization, and a re-analysis of Keren et al., 2019 illustrates the overall workflow. We provide an R package, NBFvis, to support computation and interactive visualization for the proposed dimensionality reduction approach. Code and data for reproducing experiments and analysis are available on GitHub.
2
Liu, Hailong, Xiaoguang Qiu, and Tao Jiang. "TAMI-74. SPATIOTEMPORAL MULTI-OMIC LANDSCAPE OF HUMAN MEDULLOBLASTOMA AT SINGLE CELL RESOLUTION." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi213—vi214. http://dx.doi.org/10.1093/neuonc/noab196.856.
Full textAbstract:
Abstract Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Here we use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Collectively, our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.
3
Kwon, Sang Ho, Madhavi Tippani, Abby Spangler, Heena Divecha, Kelsey Montgomery, Charles Bruce, Stephen Williams, et al. "Multi-Omic Approaches for Spatial and Pathological Registration of Gene Expression in Human Cortex." Biological Psychiatry 91, no. 9 (May 2022): S85—S86. http://dx.doi.org/10.1016/j.biopsych.2022.02.230.
Full text
4
Gonçalves, Juliana P. L., Christine Bollwein, and Kristina Schwamborn. "Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine." Life 12, no. 7 (July 12, 2022): 1037. http://dx.doi.org/10.3390/life12071037.
Full textAbstract:
Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.
5
Hsieh, James J., Natalia Miheecheva, Akshaya Ramachandran, Yang Lyu, Ilia Galkin, Viktor Svekolkin, Ekaterina Postovalova, et al. "Integrated single-cell spatial multi-omics of intratumor heterogeneity in renal cell carcinoma." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e17106-e17106. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e17106.
Full textAbstract:
e17106 Background: Clear cell renal cell carcinoma (ccRCC) exhibits conspicuous intratumor heterogeneity (ITH) - a driver of tumor evolution and metastasis. ITH in RCC has been studied extensively with bulk tumor DNA sequencing, which lacks the ability to integrate single cell resolution data, spatial architecture, and microenvironment composition. Therefore, we analyzed primary ccRCC tumors at multiple biopsy sites with CyTOF, multiplex immunofluorescence (MxIF), whole exome sequencing (WES), RNA sequencing (RNA-seq), single nuclei RNA-seq (snRNA-seq), and whole genome bisulfite sequencing (WGBS). Methods: Primary ccRCC tumors collected from 6 patients (pts) were biopsied at multiple locations and subjected to CyTOF (n = 21 sites, 6 pts), MxIF (20 markers, n = 8 sites, 3 pts), WES (n = 8 sites, 3 pts), RNA-seq (n = 8 sites, 3 pts), snRNA-seq (n = 8 sites, 3 pts), and WGBS (n = 8 sites, 3 pts), enabling integrated multi-omics analysis. MxIF, CyTOF, and genomic/transcriptomic analyses were performed by BostonGene. Results: Genomic intratumor (IT) evolution of ccRCC cells was tracked with WES, and subclonal distribution of SETD2, STAG2, TSC2 and PBRM1 mutations was observed in different IT regions. Different regions of the same tumor were similar, whereas individual patient tumors were distinct according to tumor microenvironment cellular composition measured by CyTOF or deconvoluted from RNA-seq. The cellular deconvolution of the ccRCC tumors reconstructed from RNA-seq correlated with CyTOF, snRNA-seq and WGBS, showing high concordance among the methods. The promoter CpG island methylation levels, averaged across all genes, positively correlated with ccRCC grade. MxIF revealed spatial IT heterogeneity in the distribution of immune infiltrate components. Macrophages and T cells dispersed among malignant cells; whereas, T cells formed clusters at unique tumor margins. Conclusions: The utilization of multi-omics methods produced a high-resolution portrait of the ccRCC tumor composition and identified differential ITH among regions within the primary tumors or among individual primary tumors. This study demonstrated strong concordance among the different technologies, suggesting that tumor deconvolution by bulk RNA-seq might be clinically applicable for ccRCC tumors. MxIF analysis enabled a fine elucidation of the spatial relationships among the tumor and the immune and stromal cells, missed by common omic platforms. Integrated single cell multi-omics could render specific pathobiological and therapeutic insights that impact treatment decisions.
6
Kulasinghe, Arutha, James Monkman, Honesty Kim, Aaron Mayer, Ahmed Mehdi, Nicholas Matigian, Marie Cumberbatch, et al. "Abstract 2036: Multi-omic dissection of immunotherapy response groups in non-small cell lung cancer (NSCLC)." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2036. http://dx.doi.org/10.1158/1538-7445.am2022-2036.
Full textAbstract:
Abstract Immunotherapies have led to long term benefits in a subset of non-small cell lung cancer (NSCLC) patients. Developing predictive biomarkers of response to therapy is currently an unmet clinical need. The composition of the cellular content of the tumour microenvironment (TME) is an important characteristic driving treatment resistance. In this study, we utilised spatial transcriptomics methods, including Nanostring digital spatial profiling (DSP) and multiplex IHC, to define the tumour/stroma (compartment) specific proteome and transcriptome from a cohort of 2nd line immunotherapy treated NSCLC patients. We identified by mIHC that CD68+ macrophages with PD1+, FoxP3+ cells is significantly enriched in immunotherapy resistant tumours (p=0.012). Moreover, IL2 mRNA (p=0.001) in the stromal compartment was significantly increased in patients that were sensitive to ICI therapy, whilst CTLA-4 and IDO-1 were suppressed in responsive patients. Tumour compartment localised CD44 was depleted in the responder group and corresponded inversely with higher stromal expression of one of its ligands, SPP1 (p=0.008). Orthogonal validation of CD44 by multiplex immunofluorescence confirmed both its association with response and localisation to tumour cells rather than immune cell infiltrate. Cox survival analysis showed that tumour compartment CD44 expression was associated with a poorer prognosis (HR=1.61, p=0.01) whilst stromal localisation of CTLA-4 (HR=1.78, p=0.003) and ARG1 (HR=2.37, p=0.01) were associated with poorer outcome. Through a multi-omics approach, we demonstrate the utility of spatial mapping of NSCLC tumours and provide evidence for the role of a number of compartment-specific biomarkers which may aid in identifying those likely to respond to immunotherapy. Citation Format: Arutha Kulasinghe, James Monkman, Honesty Kim, Aaron Mayer, Ahmed Mehdi, Nicholas Matigian, Marie Cumberbatch, Milan Bhagat, Rahul Ladwa, Scott Mueller, Mark Adams, Ken O'Byrne. Multi-omic dissection of immunotherapy response groups in non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2036.
7
Lou, Emil, Katherine Ladner, Kerem Wainer-Katsir, Karina Deniz, Yaara Porat, Boris Brant, Shiri Davidi, et al. "Abstract 2037: Spatial omic changes of malignant pleural mesothelioma following treatment using tumor-treating fields." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2037. http://dx.doi.org/10.1158/1538-7445.am2022-2037.
Full textAbstract:
Abstract Background: Application of alternating-electric fields as cancer-directed therapy known as Tumor-Treating Fields (TTFields) has been shown to be effective by exerting dipole alignment forces on polar microtubule subunits and dielectrophoretic forces in the cytokinetic furrow. This effect results in disruption of mitosis at the cellular level, and at the clinical level with significantly prolonged overall survival of patients with glioblastoma and malignant pleural mesothelioma (MPM). The molecular alterations that occur at the genomic and transcriptomic levels following TTFields treatment are unknown. We applied a spatial omics approach to elucidate spatial intratumoral effects of TTFields at the molecular level in regions of interest (ROI) in a mouse model of MPM. Methods: Eight Balb/C mice were injected with AB1 MPM cells until sizable tumors were observed (2-3 weeks). TTFields were applied, with heat sham used as a negative control. After 14 days (6 days of treatment, 2 days of rest, then 6 more days of treatment) tumors were resected, fixed and paraffin embedded. Following the Nanostring GeoMx protocol, thin sections of all eight tumors were placed on a slide, and incubated with both Ki-67 antibodies, and a GeoMx Mouse Cancer Transcriptome Atlas RNA probe set. Using Ki-67 staining as a guide, 12 ROIs were selected across each tumor to capture intratumoral heterogeneity, including the core and periphery of each tumor The DSP barcoded RNA probes were cleaved, sequenced and analyzed. We compared differential expression of subset gene classes of TTFields vs heat sham treated tumors; results were further stratified into Ki-67 high and low subsets for each sample. Results were assessed by Gene Set Enrichment. Results: Sham-treated tumors grew to over 300 mm3; TTFields-treated tumors averaged 100 mm3, confirming anti-tumor effect. Gene Set Enrichment Analysis uncovered upregulation of genes associated with interferon-alpha and -gamma responses in TTFields-treated tumors, which also displayed downregulation of pathway components associated with glycolysis, mTOR signaling, oxidative phosphorylation, cell invasion, hypoxia, and TNF-alpha signaling. Spatial analysis detected a heterogeneous response and differential expression between different portions of tumors. Conclusions: TTFields application induces clear patterns of differential expression in the transcriptome of treated tumors, including an increasingly immune-stimulated tumor microenvironment that also affects molecular pathways critical to cellular proliferation and invasion. These findings point the way toward improved understanding of timing and sequences of TTFields in relation to systemic cytotoxic, targeted, and immune modulation forms of therapeutic strategies. Our finding of upregulated immune response implicates TTFields as a potential synergistic tactic when coupled with immunotherapeutic approaches. Citation Format: Emil Lou, Katherine Ladner, Kerem Wainer-Katsir, Karina Deniz, Yaara Porat, Boris Brant, Shiri Davidi, Yuki Padmanabhan, Phillip Wong, Amrinder Nain, Clifford J. Steer, Moshe Giladi. Spatial omic changes of malignant pleural mesothelioma following treatment using tumor-treating fields [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2037.
8
Johnson, Brett E., Allison L. Creason, Jayne M. Stommel, Jamie M. Keck, Swapnil Parmar, Courtney B. Betts, Aurora Blucher, et al. "An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer." Cell Reports Medicine 3, no. 2 (February 2022): 100525. http://dx.doi.org/10.1016/j.xcrm.2022.100525.
Full text
9
Langston, Jordan C., Michael T. Rossi, Qingliang Yang, William Ohley, Edwin Perez, Laurie E. Kilpatrick, Balabhaskar Prabhakarpandian, and Mohammad F. Kiani. "Omics of endothelial cell dysfunction in sepsis." Vascular Biology 4, no. 1 (May 1, 2022): R15—R34. http://dx.doi.org/10.1530/vb-22-0003.
Full textAbstract:
During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein–protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis.
10
O'Byrne, Kenneth John, James Monkman, Honesty Kim, Marie Cumberbatch, Milan Bhagat, Rahul Ladwa, Mark N. Adams, and Arutha Kulasinghe. "Multi-omic and spatial dissection of immunotherapy response groups in non–small cell lung cancer (NSCLC)." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 8544. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.8544.
Full textAbstract:
8544 Background: Immune checkpoint inhibitors (ICI) have shown durable benefit in a subset of non-small cell lung cancer (NSCLC) patients. The composition of the tumour microenvironment (TME) is becomingly increasingly recognised as an important factor to predict response to therapy. Methods: Here, we applied digital spatial profiling of the tumour and stromal compartments from a 2nd line NSCLC ICI-treated cohort (n = 41 patient) and standard of care (SOC), platinum treated NSCLC cohort (n = 47), to identify tissue-based signatures of response to therapy. Results: We demonstrate by mIHC that the interaction of CD68+ macrophages with PD1+, FoxP3+ cells is significantly enriched in ICI refractory tumours (p = 0.012). Patients sensitive to ICI therapy expressed higher levels of IL2 receptor alpha (CD25, p = 0.028) within the tumour compartments, which corresponded with the increased expression of IL2 mRNA (p = 0.001) within their stroma. Immuno-inhibitory markers CTLA-4 (p = 0.021) and IDO-1 (p = 0.023) were supressed in ICI-responsive patients. Tumour CD44 (p = 0.02) was depleted in the response group and corresponded inversely with significantly higher stromal expression of one of its ligands, SPP1 (osteopontin, p = 0.008). Analysis of dysregulated transcripts indicated the potential inhibition of stromal interferon-gamma (IFNγ) activity, estrogen-receptor and Wnt-1 signalling activity within the tumour cells of ICI responsive patients. Cox survival analysis indicated tumour CD44 expression was associated with poorer prognosis (HR = 1.61, p = 0.01), consistent with its depletion in ICI sensitive patients. Similarly, stromal CTLA-4 (HR = 1.78, p = 0.003) and MDSC/M2 macrophage marker ARG1 (HR = 2.37, p = 0.01) were associated with poorer outcome while BAD (HR = 0.5, p = 0.01) appeared protective. The SOC cohort paralleled similar roles for immune checkpoints and pro-apoptotic markers, with LAG3 (HR = 3.81, p = 0.04) indicating poorer outcome, and BIM (HR = 0.16, p = 0.014) with improved outcome. Interestingly, stromal mRNA for E-selectin (HR = 652, p = 0.001), CCL17 (HR = 70, p = 0.006) and MTOR (HR = 1065, p = 0.008) were highly associated with poorer outcome in ICI treated patients, indicating pro-tumourigenic features in the tumour microenvironment that may facilitate ICI resistance. Conclusions: Through multi-modal approaches, we have dissected the characteristics of NSCLC treatment groups and provide evidence for the role of several markers including IL2, CD25, CD44 and SPP1 in the efficacy of current generations of ICI therapy.
11
Curtis, Christina. "Abstract ED7-4: Beyond the lab: Clinical implications." Cancer Research 83, no. 5_Supplement (March 1, 2023): ED7–4—ED7–4. http://dx.doi.org/10.1158/1538-7445.sabcs22-ed7-4.
Full textAbstract:
Abstract During this talk, I will review the ever expanding repertoire of single cell and spatially resolved profiling techniques which enable the interrogation breast cancer pathlogy, immuno-biology and treatment response at unprecedented resolution. I will outline considerations for throughput, plex and resolution across different methods before providing several case studies in their application. As one example, I will outline the use of multi-omic single cell profiling and spatial proteomic profiling to characterize changes throughout the course of neoadjuvant Her2-targeted therapy and leading to the identification of candidate predictive biomarkers. I will go on to discuss translational and potential clinical applications of these techniques. Citation Format: Christina Curtis. Beyond the lab: Clinical implications [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr ED7-4.
12
Holman, Derek, Stephan Rogalla, John Mark Gubatan, Samuel J. Rubin, and Mariusz Ferenc. "Su1138: INTEGRATION OF SPATIAL MULTI-OMIC IMAGING WITH MASS CYTOMETRY IDENTIFIES RARE CELL SUBSETS IN ULCERATIVE COLITIS." Gastroenterology 162, no. 7 (May 2022): S—516—S—517. http://dx.doi.org/10.1016/s0016-5085(22)61227-7.
Full text
13
Landeira-Viñuela, Alicia, Paula Díez, Pablo Juanes-Velasco, Quentin Lécrevisse, Alberto Orfao, Javier De Las Rivas, and Manuel Fuentes. "Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model." Biomolecules 11, no. 12 (November 26, 2021): 1776. http://dx.doi.org/10.3390/biom11121776.
Full textAbstract:
Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.
14
Liu, Hailong, Tao Jiang, and Xiaoguang Qiu. "Spatiotemporal multiomic landscape of human medulloblastoma at single cell resolution." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 2069. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.2069.
Full textAbstract:
2069 Background: Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Methods: We use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. Results: This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Conclusions: Our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.
15
Liu, Hanqing, Jingtian Zhou, Wei Tian, Chongyuan Luo, Anna Bartlett, Andrew Aldridge, Jacinta Lucero, et al. "DNA methylation atlas of the mouse brain at single-cell resolution." Nature 598, no. 7879 (October 6, 2021): 120–28. http://dx.doi.org/10.1038/s41586-020-03182-8.
Full textAbstract:
AbstractMammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer–gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.
16
Chew, Jennifer, Cedric Uytingco, Rapolas Spalinskas, Yifeng Yin, Joe Shuga, Benton Veire, Naishitha Anaparthy, et al. "83 Spatially resolved transcriptomic and proteomic investigation of breast cancer and its immune microenvironment." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A91. http://dx.doi.org/10.1136/jitc-2021-sitc2021.083.
Full textAbstract:
BackgroundThe tumor microenvironment (TME) is composed of highly heterogeneous extracellular structures and cell types such as endothelial cells, immune cells, and fibroblasts that dynamically influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in cancer development and progression and can significantly affect a tumor’s response to therapy and capacity for multi-drug resistance. High resolution analyses of gene and protein expression with spatial context can provide deeper insights into the interactions between tumor cells and surrounding cells within the TME, where a better understanding of the underlying biology can improve treatment efficacy and patient outcomes. Here, we demonstrated the ability to perform streamlined multi-omic tumor analyses by utilizing the 10X Genomics Visium Spatial Gene Expression Solution for FFPE with multiplex protein enablement. This technique simultaneously assesses gene and protein expression to elucidate the immunological profile and microenvironment of different breast cancer samples in conjunction with standard pathological methods.MethodsSerial (5 µm) sections of FFPE human breast cancer samples were placed on Visium Gene Expression (GEX) slides. The Visium GEX slides incorporate ~5,000 molecularly barcoded, spatially encoded capture spots onto which tissue sections are placed, stained, and imaged. Following incubation with a human whole transcriptome, probe-based RNA panel and an immuno-oncology oligo-tagged antibody panel, developed with Abcam conjugated antibodies, the tissues are permeabilized and the representative probes are captured. Paired GEX and protein libraries are generated for each section and then sequenced on an Illumina NovaSeq at a depth of ~50,000 reads per spot. Resulting reads from both libraries are aligned and overlaid with H&E-stained tissue images, enabling analysis of both mRNA and protein expression. Additional analyses and data visualizations were performed on the Loupe Browser v4.1 desktop software.ConclusionsSpatial transcriptomics technology complements pathological examination by combining histological assessment with the throughput and deep biological insight of highly-multiplexed protein detection and RNA-seq. Taken together, our work demonstrated that Visium Spatial technology provides a spatially-resolved, multi-analyte view of the tumor microenvironment, where a greater understanding of cellular behavior in and around tumors can help drive discovery of new biomarkers and therapeutic targets.
17
Markham, Nicholas O., Julia L. Drewes, Jada C. Domingue, Bob Chen, Cody N. Heiser, Molly A. Bingham, Alan J. Simmons, et al. "Sa1113: A SINGLE-CELL RESOLUTION, MULTI-OMIC SPATIAL ATLAS OF COLONIC TUMORIGENESIS DRIVEN BY C. DIFFICILE FROM HUMAN COLORECTAL CANCER-ASSOCIATED BIOFILMS." Gastroenterology 162, no. 7 (May 2022): S—310—S—311. http://dx.doi.org/10.1016/s0016-5085(22)60740-6.
Full text
18
Capra, Emanuele, and Anna Lange-Consiglio. "The Biological Function of Extracellular Vesicles during Fertilization, Early Embryo—Maternal Crosstalk and Their Involvement in Reproduction: Review and Overview." Biomolecules 10, no. 11 (November 4, 2020): 1510. http://dx.doi.org/10.3390/biom10111510.
Full textAbstract:
Secretory extracellular vesicles (EVs) are membrane-enclosed microparticles that mediate cell to cell communication in proximity to, or distant from, the cell of origin. Cells release a heterogeneous spectrum of EVs depending on their physiologic and metabolic state. Extracellular vesicles are generally classified as either exosomes or microvesicles depending on their size and biogenesis. Extracellular vesicles mediate temporal and spatial interaction during many events in sexual reproduction and supporting embryo-maternal dialogue. Although many omic technologies provide detailed understanding of the molecular cargo of EVs, the difficulty in obtaining populations of homogeneous EVs makes difficult to interpret the molecular profile of the molecules derived from a miscellaneous EV population. Notwithstanding, molecular characterization of EVs isolated in physiological and pathological conditions may increase our understanding of reproductive and obstetric diseases and assist the search for potential non-invasive biomarkers. Moreover, a more precise vision of the cocktail of biomolecules inside the EVs mediating communication between the embryo and mother could provide new insights to optimize the therapeutic action and safety of EV use.
19
Dreyer, Stephan, Nigel Jamieson, Lisa Evers, Marc Jones, Sancha Martin, Fraser Duthie, Liz Musgrove, et al. "Feasibility and clinical utility of EUS guided biopsy of pancreatic cancer for next-generation genomic sequencing." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15755-e15755. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15755.
Full textAbstract:
e15755 Background: Next-generation sequencing (NGS) has made genomic profiling to guide therapy a reality for many cancer types. The aim of this study is to investigate the feasibility of genomic profiling using standard clinical endoscopic ultrasound (EUS) core biopsy samples of Pancreatic Cancer (PC) to allow personalised cancer care. Methods:Patients undergoing EUS and biopsy for suspicion of PC underwent additional biopsies which was snap frozen. En-face frozen section enabled targeted macro-dissection prior to DNA extraction, quantification and targeted sequencing using a commercially available 151 gene ClearSeq Comprehensive Cancer Panel. Matching formalin-fixed (FFPE) diagnostic EUS biopsy and fresh frozen surgical resection specimens underwent genomic profiling for comparison. Whole genome sequencing (WGS) was performed in 2 patients. RNA sequencing was performed in samples with sufficient RNA yield. Results: Known PC genes ( KRAS, GNAS, TP53, CDKN2A, SMAD4) were identified in 27 out of 30 (90%) patients with histological diagnosis of PC. Potentially actionable somatic mutations (BRCA1, BRCA2, ATM, BRAF, JAK3) were found in 6 (20%) patients. In the 2 samples selected, WGS of the EUS samples confirmed point mutations identified on panel sequencing and revealed relevant mutational signatures and structural variation patterns. Targeted panel sequencing was successful in all FFPE samples. In 1 chemotherapy naïve patient, sequencing of a matching trio of fresh frozen and FFPE EUS biopsies, and resection sample revealed evidence of spatial intra-tumoral heterogeneity. In another patient, pre-treatment biopsy revealed a somatic BRCA1 mutation, and patient had a near complete pathological response to platinum containing neoadjuvant therapy in the resected specimen. RNA sequencing segregated patients into key clinically relevant molecular PC subtypes based on transcriptome as recently described. Conclusions: We demonstrate here novel multi-omic analysis of pancreatic cancer using standard clinical EUS guided fine needle biopsies. Multi-omic analysis of EUS biopsies offers potential clinical utility to guide personalized therapy of PC in the neoadjuvant and advanced settings.
20
Fitzgerald, Donnacha, Tobias Roider, Marc-A. Baertsch, Harald Vöhringer, Mareike Knoll, Bettina Budeus, Artur Kibler, et al. "Single-Cell Multi-Omic and Spatial Analysis of Nodal B Cell Non-Hodgkin Lymphomas Reveals Plasticity in B Cell Maturation As a Driver of Intratumor Heterogeneity." Blood 140, Supplement 1 (November 15, 2022): 752–53. http://dx.doi.org/10.1182/blood-2022-156210.
Full text
21
Schultz, Andrew R., Saeed Alahmari, Pallavi Singh, Zaid Siddiqui, Emily Thomas, Emek Demir, Laura Heiser, and Noemi Andor. "Abstract A013: Integrating imaging and sequencing to compute the subcellular organization of a cell’s transcriptome." Cancer Research 82, no. 10_Supplement (May 15, 2022): A013. http://dx.doi.org/10.1158/1538-7445.evodyn22-a013.
Full textAbstract:
Abstract Background: Incorporation of prior information in the form of pathway activity profiles was key in the success of the algorithm that won the DREAM challenge to predict in vitro cell fitness from transcriptomic and other multi-omic datasets (Costello, C. C et al, 2014). We hypothesize that leveraging additional prior information on the spatial distribution of transcriptome activity inside the cell will yield better predictions of cell fitness, which span longer timeframes. Methods: We integrated (i) sequencing and (ii) imaging data obtained from a stomach cancer cell line (NCI-N87). For (i), we used previously published scRNA-seq data available for 3,246 NCI-N87 cells. Cells were assigned to either G0/G1, S or G2M phase; and G0G1 cells were grouped into subpopulations defined by somatic copy number alterations. In addition, we calculated the pathway activity profile of each cell using gene set variation analysis. For (ii), cells were imaged on a Leica confocal SP8 using 63X objective, collecting 70 z slices of target dye and brightfield with interslice interval 0.29 µm. We trained a previously developed label-free U-Net convolutional neural network (CNN) (Ounkomol, C. et al, 2018) on Z-stacks of images containing the nuclei or mitochondria (mito) to calculate the spatial distribution of the two organelles. Models were trained for nucleus using train (N=37)/test (N=5) and mito using train (N=24)/test (N=5) with the Adam optimizer for 150,000 minibatch iterations monitoring the weighted mean squared error (MSE). The model training pipeline was implemented in PyTorch on a Nvidia DGX A100 Tesla V100 GPU. The accuracy of the model was assessed by calculating the Pearson correlation coefficient between the pixel intensities of the model’s predicted output and the independent test images. The predicted 3D organelles were used as input for segmentation using the Cellpose algorithm (String, C. et al, 2021), giving us nucleus and mito coordinates (X,Y,Z) for each cell. To integrate (i) and (ii) we overlaid the distributions of nucleus and mito area and volume onto the activity of pathways expressed in the nucleus, mito, and their respective membranes. Sequenced and imaged NCI-N87 cells were then co-clustered together to obtain a tree that links profiles between the two assays. Results: Overall, the correlation coefficient (r) was higher when using nucleus images for training (r=[0.759, 0.833]; average 0.780) compared to mito (r=[0.633 - 0.783]; average 0.680), even when the sample sizes were equivalent. Of the imaged cells detected within a given field of view, 50-80% were linked to a sequenced cell. Linking sequenced and imaged cells allows visualizing the spatial distribution of pathway activity among various organelles inside a cell. Conclusions: While our results demonstrate how this can be achieved in principle computationally, they will require extensive experimental validation. Doing so will transform omics-based predictions of cell fitness into problems that can be solved by image classification algorithms and recent advances in computer vision. Citation Format: Andrew R. Schultz, Saeed Alahmari, Pallavi Singh, Zaid Siddiqui, Emily Thomas, Emek Demir, Laura Heiser, Noemi Andor. Integrating imaging and sequencing to compute the subcellular organization of a cell’s transcriptome [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A013.
22
Schueder, Florian, and Joerg Bewersdorf. "Omics goes spatial epigenomics." Cell 185, no. 23 (November 2022): 4253–55. http://dx.doi.org/10.1016/j.cell.2022.10.014.
Full text
23
LeMieux, Julianna. "Spatial The Next Omics Frontier." Genetic Engineering & Biotechnology News 40, no. 10 (October 1, 2020): 18–20. http://dx.doi.org/10.1089/gen.40.10.07.
Full text
24
Deng, Yanxiang, Marek Bartosovic, Petra Kukanja, Di Zhang, Yang Liu, Graham Su, Archibald Enninful, Zhiliang Bai, Gonçalo Castelo-Branco, and Rong Fan. "Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level." Science 375, no. 6581 (February 11, 2022): 681–86. http://dx.doi.org/10.1126/science.abg7216.
Full textAbstract:
Spatial omics emerged as a new frontier of biological and biomedical research. Here, we present spatial-CUT&Tag for spatially resolved genome-wide profiling of histone modifications by combining in situ CUT&Tag chemistry, microfluidic deterministic barcoding, and next-generation sequencing. Spatially resolved chromatin states in mouse embryos revealed tissue-type-specific epigenetic regulations in concordance with ENCODE references and provide spatial information at tissue scale. Spatial-CUT&Tag revealed epigenetic control of the cortical layer development and spatial patterning of cell types determined by histone modification in mouse brain. Single-cell epigenomes can be derived in situ by identifying 20-micrometer pixels containing only one nucleus using immunofluorescence imaging. Spatial chromatin modification profiling in tissue may offer new opportunities to study epigenetic regulation, cell function, and fate decision in normal physiology and pathogenesis.
25
Palla, Giovanni, Hannah Spitzer, Michal Klein, David Fischer, Anna Christina Schaar, Louis Benedikt Kuemmerle, Sergei Rybakov, et al. "Squidpy: a scalable framework for spatial omics analysis." Nature Methods 19, no. 2 (January 31, 2022): 171–78. http://dx.doi.org/10.1038/s41592-021-01358-2.
Full textAbstract:
AbstractSpatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
26
Fan, Rong, and Omer Bayraktar. "Special Issue: Spatial Omics." GEN Biotechnology 2, no. 1 (February 1, 2023): 3–4. http://dx.doi.org/10.1089/genbio.2023.29076.cfp.
Full text
27
Drago, Joshua, Zonera Hassan, Jan Zaucha, Ansh Kapil, Fatemeh Derakhshan, Fresia Pareja, Shimulov Anatoliy, et al. "Abstract P2-09-03: Quantification of HER2 expression and spatial biology using computational pathology: A cross-assay validation study in breast cancer." Cancer Research 83, no. 5_Supplement (March 1, 2023): P2–09–03—P2–09–03. http://dx.doi.org/10.1158/1538-7445.sabcs22-p2-09-03.
Full textAbstract:
Abstract Background Conventional pathologic scoring of HER2 by IHC is proven to distinguish potential responders to trastuzumab but has not been effective for next generation antibody drug conjugates (ADCs) such as trastuzumab deruxtecan (T-DXd), which is capable of bystander killing. Several alternative approaches have been deployed to measure HER2, including immunofluorescence and mRNA sequencing. We have developed a novel and fully automated computational pathology technique, Quantitative Continuous Scoring (QCS), to quantify the level and distribution of HER2 from digitized HER2 IHC slides in an objective, quantifiable, and reproducible manner on a per-cell basis [Gustavson et al., SABCS 2020]. To further validate this approach, we performed a systematic multi-omic comparison of QCS to orthogonal methods of HER2 quantitation on a cohort of primary and metastatic breast cancer cases (N=30). Methods HER2 was evaluated using three independent methods on serial tissue sections obtained from 30 archival FFPE breast cancer samples distributed over the full range of HER2 expression, from 0 to 3+. HER2-IHC staining (clone 4B5, Roche Tissue Diagnostics) was performed using standard methods and cases were scored by two pathologists using CAP/ASCO guidelines and H-scores were assigned. We performed FISH (HER2 IQFISH pharmDx [Dako]; PathVysion HER-2 DNA Probe Kit [Vysis]), mRNA quantification of ERBB2 transcript levels (Nano String), and immunofluorescence (IF; HER2 clone 29D8, CST). Imaged with Vectra (Akoya) and analyzed with Halo (Indica). QCS readouts were generated from the above-mentioned digital images of IHC slides by using a fully automated image analysis pipeline; readouts included per-cell staining intensity measurements of membranes and cytoplasmic sub-compartments in terms of optical density (OD) [Van der Laak, JQCS 2000], which were aggregated to a single slide-level score. Additionally, using the OD measurements and the cell locations, a Spatial Proximity Score (SPS) was computed, summing the percentage of cells with OD≥10 (corresponding to the limit of visual detection of IHC staining) as well as the percentage of cells with OD< 10 within a prespecified radius (25µm) of a neighboring cell with OD≥10. Results Our analysis demonstrated that QCS-based scoring correlates with orthogonal measurements used in this study. Comparing protein-based assays, the observed Pearson correlation was R=0.88 between QCS median membrane OD and IHC H-scores, R=0.86 with IF-based HER2 mean cell expression intensity, and R=0.85 with IF-based H-scores. Correlation with transcriptomic profiling was R=0.81 for OD vs. mRNA, however ERBB2 transcript levels did not distinguish between HER2 0, 1+, and 2+ FISH negative cases, while QCS was able to do so. Correlation between protein-based and nucleic-acid based assays were numerically worse, with R=0.64 for OD vs. FISH. All samples (including those with HER2 IHC scores of 0 and H-Scores < 10) had at least ~20% of cells with quantifiable HER2 expression by OD, the presence of which was confirmed using IF. For cases in the lowest quartile of HER2 expression by OD, SPS identified 20-50% additional HER2-null cells that were in close proximity to HER2-expressing cells that may be vulnerable to bystander killing. Conclusion QCS-based scoring is consistent with orthogonal protein-based measurements across the range of HER2 expression. Most importantly, QCS derived-spatial analysis features identify additional patients in the lower end of HER2 expression that might be highly relevant for ADC response prediction, particularly if a drug exerts bystander activity. Further clinical verification and validation on large cohorts is needed. Footnote: This study was approved by the IRB at MSKCC. Citation Format: Joshua Drago, Zonera Hassan, Jan Zaucha, Ansh Kapil, Fatemeh Derakhshan, Fresia Pareja, Shimulov Anatoliy, Fanni Ratzon, Travis J Hollman, Claire Myers, Jessica Chan, Andrea Spitzmuller, Mark Gustavson, Danielle Carroll, Dara Ross, Jorge Reis-Filho, Carl barrett, Sihem Khalifa, Schmidt Guenter, Hadassah Sade, Sarat Chandarlapaty. Quantification of HER2 expression and spatial biology using computational pathology: A cross-assay validation study in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-09-03.
28
Vermeulen, I., T. Dankcer, G. Hoogland, K. Rijkers, O. Schijns, B. Balluff, E. Cuypers, and B. Cillero-Pastor. "Multimodal spatial omics in human focal epilepsy." Brain and Spine 2 (2022): 101583. http://dx.doi.org/10.1016/j.bas.2022.101583.
Full text
29
Schueder, Florian, Eduard M. Unterauer, Mahipal Ganji, and Ralf Jungmann. "DNA‐Barcoded Fluorescence Microscopy for Spatial Omics." PROTEOMICS 20, no. 23 (October 26, 2020): 1900368. http://dx.doi.org/10.1002/pmic.201900368.
Full text
30
Kaplan, Henry G., Alex Barrett, Jiaxin Niu, Somasundaram Subramaniam, and Maria Matsangou. "Expanding the molecular taxonomy of NUT midline carcinomas with multiomic analyses." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e21008-e21008. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e21008.
Full textAbstract:
e21008 Background: NUT midline carcinoma (NMC) is an aggressive squamous cell carcinoma molecularly defined by a chromosomal rearrangement of nuclear protein in testis (NUTM1) with bromodomain-containing protein 3 or 4 (BRD3/4). While NMCs are characterized by this rare canonical gene rearrangement little is known about the transcriptome and proteosome of this rare disease. As such, we set out to comprehensively characterize five NMC cases in which we attained targeted DNA sequencing, full-transcriptome RNA sequencing, and targeted proteomics. We further examine and integrate these results in order to better understand the relationship between gene expression and protein abundance within the context of NMC. Methods: All cases were analyzed for genomic and transcriptomic alterations against a custom panel via the Tempus xT tissue biopsy assay (DNA sequencing of 648 genes in tumor and matched normal samples at 500x depth and full-transcriptome RNA sequencing) for germline and/or somatic mutations. The xT assay detects single nucleotide variants, specific insertion/deletions, amplifications and gene fusions, as well as tumor mutational burden (TMB) and microsatellite instability (MSI) status. Proteomic data were obtained utilizing digital spatial profiling through Nanostring immune, MAPK and PI3/AKT, and pan tumor nCounter GeoMix panels. Results: Clinical characteristics, histology, and genomic/proteomic alterations for 5 NMC cases are presented. Cases were defined by pathological assessment and the identification of the canonical NUTM1 fusion, further broken down by fusion partner with three patients having NUTM1-BRD4 fusions, one NUT-BRD3, and one NUT-ZMYND8. TMBs ranged for 0.8-.6 mutations/megabases (n=5). All patients were MSI stable (5/5). Of three patients with available PD-L1 IHC result, one had elevated PD-L1 tumor staining at 70%. Results will be presented from full-transcriptome RNA expression analysis indicating overexpression of BRAF, MYC, mTOR, and EGFR, among others. Targeted proteomics were performed to assess relative abundance at the protein level (results to be presented). Clinical follow up for the five patients revealed that two have survived beyond 7 months. A lung primary patient treated with surgical resection and post op radiation (XRT) is NED at 63 months. A sinus primary patient is NED at 16 months after a partial response (PR) to taxotere/5FU/Cisplatin followed by resection and XRT/cis platin. One patient had a brief PR from ifosphamide/etoposide/vorinostat. One patient's tumor grew through XRT/cisplatin. Conclusions: Multi-omic analysis has the potential to further elucidate the mechanisms of tumor growth in NMC and identify new targets for the treatment of this aggressive and poor prognosis disease.
31
Brinkmann, N., F. Wyrowski, J. Kauffmann, D. Colombo, K. M. Menten, X. D. Tang, and R. Güsten. "An imaging line survey of OMC-1 to OMC-3." Astronomy & Astrophysics 636 (April 2020): A39. http://dx.doi.org/10.1051/0004-6361/201936885.
Full textAbstract:
Context. Recently, sensitive wide-bandwidth receivers in the millimetre regime have enabled us to combine large spatial and spectral coverage for observations of molecular clouds. The resulting capability to map the distributions of lines from many molecules simultaneously yields unbiased coverage of the various environments within star-forming regions. Aims. Our aim is to identify the dominant molecular cooling lines and characteristic emission features in the 1.3 mm window of distinct regions in the northern part of the Orion A molecular cloud. By defining and analysing template regions, we also intend to help with the interpretation of observations from more distant sources which cannot be easily spatially resolved. Methods. We analyse an imaging line survey covering the area of OMC-1 to OMC-3 from 200.2 to 281.8 GHz obtained with the PI230 receiver at the APEX telescope. Masks are used to define regions with distinct properties (e.g. column density or temperature ranges) from which we obtain averaged spectra. Lines of 29 molecular species (55 isotopologues) are fitted for each region to obtain the respective total intensity. Results. We find that strong sources like Orion KL have a clear impact on the emission on larger scales. Although not spatially extended, their line emission contributes substantially to spectra averaged over large regions. Conversely, the emission signatures of dense, cold regions like OMC-2 and OMC-3 (e.g. enhanced N2H+ emission and low HCN/HNC ratio) seem to be difficult to pick up on larger scales, where they are eclipsed by signatures of stronger sources. In all regions, HCO+ appears to contribute between 3 and 6% to the total intensity, the most stable value for all bright species. N2H+ shows the strongest correlation with column density, but not with typical high-density tracers like HCN, HCO+, H2CO, or HNC. Common line ratios associated with UV illumination, CN/HNC and CN/HCO+, show ambiguous results on larger scales, suggesting that the identification of UV illuminated material may be more challenging. The HCN/HNC ratio may be related to temperature over varying scales.
32
Ahmed, Rashid, Robin Augustine, Enrique Valera, Anurup Ganguli, Nasrin Mesaeli, Irfan S. Ahmad, Rashid Bashir, and Anwarul Hasan. "Spatial mapping of cancer tissues by OMICS technologies." Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1877, no. 1 (January 2022): 188663. http://dx.doi.org/10.1016/j.bbcan.2021.188663.
Full text
33
Eggeling, Ferdinand, and Franziska Hoffmann. "Microdissection—An Essential Prerequisite for Spatial Cancer Omics." PROTEOMICS 20, no. 17-18 (July 6, 2020): 2000077. http://dx.doi.org/10.1002/pmic.202000077.
Full text
34
Disselhorst, Jonathan A., Marcel A. Krueger, S. M. Minhaz Ud-Dean, Ilja Bezrukov, Mohamed A. Jarboui, Christoph Trautwein, Andreas Traube, et al. "Linking imaging to omics utilizing image-guided tissue extraction." Proceedings of the National Academy of Sciences 115, no. 13 (March 5, 2018): E2980—E2987. http://dx.doi.org/10.1073/pnas.1718304115.
Full textAbstract:
Phenotypic heterogeneity is commonly observed in diseased tissue, specifically in tumors. Multimodal imaging technologies can reveal tissue heterogeneity noninvasively in vivo, enabling imaging-based profiling of receptors, metabolism, morphology, or function on a macroscopic scale. In contrast, in vitro multiomics, immunohistochemistry, or histology techniques accurately characterize these heterogeneities in the cellular and subcellular scales in a more comprehensive but ex vivo manner. The complementary in vivo and ex vivo information would provide an enormous potential to better characterize a disease. However, this requires spatially accurate coregistration of these data by image-driven sampling as well as fast sample-preparation methods. Here, a unique image-guided milling machine and workflow for precise extraction of tissue samples from small laboratory animals or excised organs has been developed and evaluated. The samples can be delineated on tomographic images as volumes of interest and can be extracted with a spatial accuracy better than 0.25 mm. The samples remain cooled throughout the procedure to ensure metabolic stability, a precondition for accurate in vitro analysis.
35
Mezger, Stephanie T. P., Alma M. A. Mingels, Otto Bekers, Ron M. A. Heeren, and Berta Cillero-Pastor. "Mass Spectrometry Spatial-Omics on a Single Conductive Slide." Analytical Chemistry 93, no. 4 (January 7, 2021): 2527–33. http://dx.doi.org/10.1021/acs.analchem.0c04572.
Full text
36
Lewis, Sabrina M., Marie-Liesse Asselin-Labat, Quan Nguyen, Jean Berthelet, Xiao Tan, Verena C. Wimmer, Delphine Merino, Kelly L. Rogers, and Shalin H. Naik. "Spatial omics and multiplexed imaging to explore cancer biology." Nature Methods 18, no. 9 (August 2, 2021): 997–1012. http://dx.doi.org/10.1038/s41592-021-01203-6.
Full text
37
Schueder, Florian, Eduard M. Unterauer, Mahipal Ganji, and Ralf Jungmann. "Front Cover: DNA‐Barcoded Fluorescence Microscopy for Spatial Omics." PROTEOMICS 20, no. 23 (December 2020): 2070161. http://dx.doi.org/10.1002/pmic.202070161.
Full text
38
KLEIBER, GEORGES, and FRANCINE GERHARD-KRAIT. "Quelque part: du spatial au non spatial en passant par l'indétermination et la partition." Journal of French Language Studies 16, no. 2 (June 15, 2006): 147–66. http://dx.doi.org/10.1017/s0959269506002407.
Full textAbstract:
Notre objectif est de décrire l'évolution de la locution adverbiale quelque part de son emploi spatial standard à des emplois non spatiaux en vogue à l'heure actuelle, mais non encore analysés, comme ceux qu'exemplifient les séquences:Moi, ça m'embête quelque part de dire à un enfant euh non tu parles pas comme ça. Quelque part, il y a eu un abandon de la structure éducative (propos d'un député UMP sur le rôle des parents et de l'école dans la prévention de la fugue et de l'école buissonnière)Je sais que Brian n'est pas mon enfant, mais quelque part il l'est aussi (propos d'une mère dont la fille de 15 ans a eu un enfant et que la mère aide à élever, 27/01/03, émission TV)(Quelque part, ça m'interpelle/m'attriste/me dérange/me chagrine, (relevé à l'oral)Dans une première partie, nous analyserons le ‘sens’ spatial en mettant plus particulièrement en relief les deux propriétés qui le caractérisent, notamment le trait de ‘partition’ généralement omis dans les descriptions. Après avoir distingué les emplois non spatiaux des emplois spatiaux, nous essaierons dans notre deuxième partie: de faire ressortir deux types d'emplois non spatiaux,d'expliquer ces emplois, dans une perspective de grammaticalisation, à partir des deux traits sémantiques postulés pour les emplois spatiaux,de décrire les effets de sens auxquels ils donnent lieu et qui expliquent en partie leur succès.
39
Zhou, Xiao Hu. "Correcting Synchronous Scanning OMIS Remote Sensing Images Using the Spatial Orientation Data of the Inertial Navigation System." Applied Mechanics and Materials 513-517 (February 2014): 2867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2867.
Full textAbstract:
Inertial navigation system using IMU (Inertial Measurement Unit) of the flying space positioning data POS (Position & Orientation System) synchronized scanning of the hyperspectral remote sensing OMIS (Operational Modular Imaging Spectrometer) image correction, obtaining from the IMU in sync with the attitude parameter OMIS , the coordinate transformation parameter calculation and flight attitude, according to OMIS imaging principle of mathematical calibration model, the corrected image pixel re-sampling, the image correction, and achieved better image processing results.
40
Pandele, Alina, Alison Woodward, Sophie Lankford, Donald Macarthur, Ian Kamaly-Asl, David Barrett, Richard Grundy, Dong-Hyun Kim, and Ruman Rahman. "TAMI-76. INTEGRATED MULTI-OMICS REVEAL INTRATUMOUR HETEROGENEITY AND NOVEL THERAPEUTIC TARGETS IN PAEDIATRIC EPENDYMOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi214. http://dx.doi.org/10.1093/neuonc/noab196.858.
Full textAbstract:
Abstract Ependymoma (EPN) is the second most common malignant paediatric brain tumour with a five-year survival rate of only 25% following relapse. While molecular heterogeneity between EPN tumours is well understood, little is known concerning spatially-distinct intratumour heterogeneity within patients. In this context, we present a multi-omics integration of expression data at transcriptomic and metabolomic levels revealing intratumour heterogeneity and novel therapeutic targets. Surgically resected ependymoma tissue from two epigenetic subgroups, posterior fossa-A (PF-A) and supratentorial RELA, were first homogenised and polar metabolites, lipids and RNA simultaneously extracted from the same cellular population. Using liquid chromatography-mass spectrometry (LC-MS) and RNAseq 115 metabolites and 1580 upregulated genes were identified between the two subgroups, therefore validating previously reported genetic clustering of these two subtypes. Sampling of anatomically distinct regions was performed between eight PF-A EPN patients and multi-omic data was compared across 28 intratumour regions, with at least 3 different regions per patient. Integration of genes and metabolites revealed 124 dysregulated metabolic pathways, encompassing 156 genes and 49 metabolites. A large number of interactions occur in the gluconeogenesis and glycine pathways in 6 out of 8 patients, putatively representing therapeutically relevant ubiquitous metabolic pathways critical for EPN survival. Each anatomical region also presented at least one unique gene-metabolite interaction demonstrating heterogeneity within and across PF-A EPN tumours. A subset of the eight most prevalent genes across patients (GAD1, NT5C, FBP1, FMO3, HK3, TALDO1, NT5E, ALDH3A1) were selected for in vitro metabolic assays using 10 repurposed cytotoxic agents against PF-A EPN cell lines derived from intratumour regions of the same patient. 5/8 genes map within the gluconeogenesis metabolic pathway, further highlighting its significance within PF-A EPN. This is the first instance where multi-omic data integration and intratumour heterogeneity has been investigated for paediatric EPN revealing novel potential targets in the context of gene-metabolite correlations.
41
Dong, Xianjun, Chunyu Liu, and Mikhail Dozmorov. "Review of multi-omics data resources and integrative analysis for human brain disorders." Briefings in Functional Genomics 20, no. 4 (May 8, 2021): 223–34. http://dx.doi.org/10.1093/bfgp/elab024.
Full textAbstract:
Abstract In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer’s disease, Parkinson’s disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.
42
Su, Graham, Xiaoyu Qin, Archibald Enninful, Zhiliang Bai, Yanxiang Deng, Yang Liu, and Rong Fan. "Spatial multi-omics sequencing for fixed tissue via DBiT-seq." STAR Protocols 2, no. 2 (June 2021): 100532. http://dx.doi.org/10.1016/j.xpro.2021.100532.
Full text
43
Wu, Yi-Chien, and Steve Seung-Young Lee. "Abstract 74: Single-cell 3D spatial omics for tumor hypoxia." Cancer Research 82, no. 12_Supplement (June 15, 2022): 74. http://dx.doi.org/10.1158/1538-7445.am2022-74.
Full textAbstract:
Abstract Tumor hypoxia arises from the abnormal vasculature in tumors which limit the oxygen supply to the rapidly growing cancer cells. To acclimate to this deprived microenvironment, cancer cells upregulate hypoxia-inducible factor 1-alpha (HIF-1) signaling pathway priming themselves towards more aggressive and heterogeneous phenotypes. Hence, tumor hypoxia becomes a major obstacle for effective cancer treatment. Deciphering hypoxia-driven tumor heterogeneity within the tumors is essential to understanding cancer biology and developing novel therapeutic strategies. A stronger focus should be directed towards the effect of hypoxia types and levels rather than just the absence or presence of hypoxia since hypoxia is heterogeneous in the three-dimensional (3D) tumor microenvironment (TME). There are acute and chronic types of tumor hypoxia, and the hypoxia levels are also variable, forming unpredictable and irregular 3D hypoxic areas. Although traditional techniques (i.e., single-cell RNA sequencing (scRNA-seq), immunohistochemistry (IHC), and immunofluorescence (IF)) and innovative spatial transcriptomics have been developed and applied for investigating tumor cell heterogeneity. Underlying issue of missing spatial information, limited numbers of the biomarker detection, underrepresentation of 3D tumor structure, or limited analyzed area onto the array chip size exist respectively in these methods. The objective of this project is to develop a 3D spatial genomics approach with single-cell resolution, named sc3DSG, as an advanced spatial transcriptomic method to study hypoxia-driven cancer cell heterogeneity in 3D. Given that, I developed a 3D cellular fluorescence barcoding method by microscopic photobleaching in tumor macro-sections. The barcodes would maintain a fluorescent footprint of cells in different hypoxic areas upon tissue dissociation. Cells encoded with varied barcodes could be differentiated on flow cytometry and further sorted into separate groups for scRNA-seq. With the optimization with RNA preservation reagent in the workflow, sc3DSG achieves high-quality and high-yield RNA transcript for unbiased transcriptomic analysis. In summary, sc3DSG is an advanced spatial transcriptomic method including a sequential workflow of photobleaching barcoding, tissue dissociation, fluorescence-activated cell sorting (FACS), and scRNA-seq. Sc3DSG enables the study of tissue molecular features without losing spatial information and is a better fit for the study of complex 3D structure like vessels or tumor hypoxia. This approach will unravel hypoxia-driven bimolecular and cellular features by considering the degrees and types of tumor hypoxia. Citation Format: Yi-Chien Wu, Steve Seung-Young Lee. Single-cell 3D spatial omics for tumor hypoxia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 74.
44
Eggeling, Ferdinand, and Franziska Hoffmann. "Front Cover: Microdissection—An Essential Prerequisite for Spatial Cancer Omics." PROTEOMICS 20, no. 17-18 (September 2020): 2070131. http://dx.doi.org/10.1002/pmic.202070131.
Full text
45
Mezger, Stephanie T. P., Alma M. A. Mingels, Matthieu Soulié, Carine J. Peutz-Kootstra, Otto Bekers, Paul Mulder, Ron M. A. Heeren, and Berta Cillero-Pastor. "Protein Alterations in Cardiac Ischemia/Reperfusion Revealed by Spatial-Omics." International Journal of Molecular Sciences 23, no. 22 (November 10, 2022): 13847. http://dx.doi.org/10.3390/ijms232213847.
Full textAbstract:
Myocardial infarction is the most common cause of death worldwide. An understanding of the alterations in protein pathways is needed in order to develop strategies that minimize myocardial damage. To identify the protein signature of cardiac ischemia/reperfusion (I/R) injury in rats, we combined, for the first time, protein matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and label-free proteomics on the same tissue section placed on a conductive slide. Wistar rats were subjected to I/R surgery and sacrificed after 24 h. Protein MALDI-MSI data revealed ischemia specific regions, and distinct profiles for the infarct core and border. Firstly, the infarct core, compared to histologically unaffected tissue, showed a significant downregulation of cardiac biomarkers, while an upregulation was seen for coagulation and immune response proteins. Interestingly, within the infarct tissue, alterations in the cytoskeleton reorganization and inflammation were found. This work demonstrates that a single tissue section can be used for protein-based spatial-omics, combining MALDI-MSI and label-free proteomics. Our workflow offers a new methodology to investigate the mechanisms of cardiac I/R injury at the protein level for new strategies to minimize damage after MI.
46
Velten, Britta, Jana M. Braunger, Ricard Argelaguet, Damien Arnol, Jakob Wirbel, Danila Bredikhin, Georg Zeller, and Oliver Stegle. "Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO." Nature Methods 19, no. 2 (January 13, 2022): 179–86. http://dx.doi.org/10.1038/s41592-021-01343-9.
Full textAbstract:
AbstractFactor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics.
47
Tisseyre, B., and C. Leroux. "How significantly different are your within field zones?" Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 620–24. http://dx.doi.org/10.1017/s2040470017000012.
Full textAbstract:
A classical approach in precision agriculture consists in validating within field zones defined from high spatial resolution observations by agronomic information (AI). Zones validation generally involves a two-step process. First, AI are obtained on a regular grid or following a target sampling strategy inside the field. Then, a statistical test, most often an ANOVA, is used to determine if the management zones created with the high spatial resolution auxiliary data explain differences in the AI values. Unfortunately, in precision agriculture, many of the works using such an approach omit a necessary condition for the implementation of the aforementioned ANOVA test, i.e. the observations need to be independent from each other. This condition is unfortunately seldom satisfied since AI are often spatially auto-correlated. In order to highlight this problem, simulated datasets with different and known AI spatial autocorrelation were used. Results show that as AI are more and more spatially auto-correlated, ANOVA tests almost always conclude that the management zones obtained with auxiliary data are significant whatever the zoning, i.e. even a completely random one. To overcome this problem, the paper introduces two methods directly inspired from published works in the field of ecology. Two cases were considered: the first one applies when large AI dataset (n>40) is available and the other one applies for small AI dataset (n<40). Both methods are implemented on a real precision viticulture example.
48
Mathur, Radhika, Qixuan Wang, Patrick Schupp, Ana Nikolic, Takafumi Yamaguchi, Stephanie Hilz, Chibo Hong, et al. "Abstract 3621: 3D spatial sampling and integrated omics reveal sources and patterning of intratumoral heterogeneity in glioblastoma." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3621. http://dx.doi.org/10.1158/1538-7445.am2022-3621.
Full textAbstract:
Abstract Treatment failure in glioblastoma, the most common and lethal primary brain tumor in adults, is often attributed to intratumoral heterogeneity as it fosters tumor evolution and selection of resistant clones. Sources of intratumoral heterogeneity may include genomic alterations, such as single-nucleotide and structural variants, and epigenomic alterations, such as changes in chromatin structure and transcriptional regulation. The relative extent and functional significance of these contributors to intratumoral heterogeneity in glioblastoma have yet to be elucidated. In collaboration with neurosurgeons and neuro-imaging experts, we have established a novel approach towards characterizing intratumoral heterogeneity in three-dimensional (3D) space. For patients undergoing tumor resection, we utilize 3D surgical neuronavigation to safely acquire ~10 samples representing maximal anatomical diversity. Samples are mapped by 3D spatial coordinates and integrated with patient MRI scans for 360º visualization of sample location in context of the brain. We have now conducted whole-exome sequencing (Exome-Seq), assay for transposase-accessible chromatin (ATAC-Seq), and RNA-sequencing (RNA-Seq) for 83 spatially mapped samples obtained from 8 patients with primary IDH-WT glioblastoma. Integrative data analysis provides unprecedented insight into sources of intratumoral heterogeneity in glioblastoma and their 3D spatial patterning. We find that tumor cells show aberrant transcription factor activity, activation of fetal brain programs, and variable chromatin accessibility at CTCF-binding loop anchors indicating plasticity in higher-order chromatin structure. Chromosome conformation capture analysis by Hi-C extends these findings and reveals intratumoral differences in long-range chromatin interactions due to structural variants and enhancer hijacking. Further, we use deconvolution to identify microenvironmental contributors to epigenomic intratumoral heterogeneity including neural, glial, and immune populations. We define chromatin signatures associated with microenvironmental cell types and states, revealing their 3D spatial patterning, and validate these findings by single-cell ATAC-Seq. Our work thus establishes both tumor and microenvironmental sources of intratumoral heterogeneity in glioblastoma, revealing their chromatin programs and 3D spatial patterning. As a resource for further investigation, we have developed an interactive data sharing platform that enables visualization of brain tumor intratumoral heterogeneity in 360 degrees. Citation Format: Radhika Mathur, Qixuan Wang, Patrick Schupp, Ana Nikolic, Takafumi Yamaguchi, Stephanie Hilz, Chibo Hong, Ivan Smirnov, Marisa LaFontaine, Joanna Phillips, Susan Chang, Yan Li, Janine Lupo, Paul Boutros, Marco Gallo, Michael Oldham, Feng Yue, Joseph Costello. 3D spatial sampling and integrated omics reveal sources and patterning of intratumoral heterogeneity in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3621.
49
DeRosa, James. "Setting a New Standard for Spatial Omics: An Integrated Multiomics Approach." Genetic Engineering & Biotechnology News 42, S1 (May 1, 2022): 26–28. http://dx.doi.org/10.1089/gen.42.s1.07.
Full text
50
Liu, Yang, Mingyu Yang, Yanxiang Deng, Graham Su, Archibald Enninful, Cindy C. Guo, Toma Tebaldi, et al. "High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue." Cell 183, no. 6 (December 2020): 1665–81. http://dx.doi.org/10.1016/j.cell.2020.10.026.
Full text