Relevant bibliographies by topics / Spatial omics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spatial omics'

Author: Grafiati
Published: 1 February 2025

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Journal articles on the topic "Spatial omics"

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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.

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Lee, Sumin, Amos C. Lee, and Sunghoon Kwon. "Abstract 5639: High throughput spatially resolved laser-activated cell sorting links the genomic molecules with its spatial information." Cancer Research 83, no. 7_Supplement (April 4, 2023): 5639. http://dx.doi.org/10.1158/1538-7445.am2023-5639.

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Abstract Spatial omics profiling technologies have been recognized recently for its ability to decipher the genetic molecules that are structurally relevant in pathology. Especially, in tumor biology, tumor is not the group of malignant tumor cells, but rather group of various cells such as tumor cells, immune cells, fibroblasts, etc. gathers together, constructing the tumor microenvironments. Technologies to analyze such microstructures have evolved from bulk sequencing, single cell sequencing to spatial omics profiling technologies. Spatial omics profiling technologies have highly influenced in decoding cancerous mechanisms by questioning the tumor heterogeneity, tumor microenvironment and spatial biomarkers. Most of the spatial omics technologies focus on mapping the spatial omics landscape in a large scale. They rather introduces the spatially-barcoded capture probes or fluorescence labeled target probes to spatially locate the genetic molecules. The information depth and the scalability of the techniques varies according to the purpose of the spatial assay techniques. Such technologies are capable of discovering the spatial heterogeneity and the spatial landscape of the consisting cell types due to relatively low depth of the omics information. To effectively address the target molecules for therapeutics or diagnostics, higher depth of the omics information are required. To meet the needs, region of interest (ROI)-based spatial technologies isolated the target regions and applies chemistries for higher coverage omics data. Conventional cell sorters utilizes microfluidic channels to sort cells of interest which requires cell dissociation in a solution phase. For instance, Fluorescence activated cell sorter (FACS) or Magnetic-activated cell sorting (MACS) uses fluorescence or magnetic particles, respectively, to designate the cells of interest in dissociated cell solutions. Spatially isolating techniques such as laser capture microdissection (LCM) is able to sort out the ROIs while preserving the spatial context, but it approximately takes an hour for isolating the targets. Also, it uses rather UV laser to dissect out cells or IR-activated melting of polymers to stick out cells which might cause damage to cells. Here, I developed the automated spatially resolved laser activated cell sorter that isolates the cells in target per second while preserving the spatial context of the cells. Specific region of indium tin oxide (ITO) coated slide glass evaporates when illuminated by IR laser pulse, plunging the cells into the desired reservoir. The applicability of the suggested cell sorter are demonstrated in omics profiling chemistries such as DNA sequencing, RNA sequencing, mass spectrometry, etc. Citation Format: Sumin Lee, Amos C. Lee, Sunghoon Kwon. High throughput spatially resolved laser-activated cell sorting links the genomic molecules with its spatial information. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5639.
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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.

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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.
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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.

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Moses, Lambda. "From Geospatial to Spatial -Omics." XRDS: Crossroads, The ACM Magazine for Students 30, no. 2 (December 2023): 16–19. http://dx.doi.org/10.1145/3637459.

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When Los Angeles is mentioned, cycling is usually not the first thing that comes to mind. However, during my past 10 years in LA studying molecular biology and bioinformatics, my bike trips through the geographical space of LA have inspired many ideas in my research in spatial data analysis in bioinformatics. I have written software to bring decades of research in geospatial data analysis to spatial -omics, as my trips make me ponder on spatial phenomena in general.
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Kim, Meeri. "Mapping Biology with Spatial Omics." Optics and Photonics News 35, no. 4 (April 1, 2024): 26. http://dx.doi.org/10.1364/opn.35.4.000026.

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Ma, Yanxia, Nhat Nguyen, Sanjay Singh, Akshay Basi, Duncan Mak, Javier Gomez, Jared Burks, Erin Seely, Frederick Lang, and Chibawanye Ene. "EPCO-07. INTEGRATING SPATIALLY RESOLVED MULTI-OMICS DATA TO UNCOVER DYSFUNCTIONAL METABOLISM DRIVEN NETWORKS THAT ENHANCE INFILTRATION OF DIFFUSE GLIOMAS." Neuro-Oncology 26, Supplement_8 (November 1, 2024): viii2. http://dx.doi.org/10.1093/neuonc/noae165.0007.

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Abstract BACKGROUND Diffuse infiltration is an aggressive feature of high-grade gliomas with survival implications. The contribution of crosstalk between non-neoplastic and neoplastic cells to tumor infiltration remains largely understudied due to the lack of profiling techniques that retain spatial information. Spatial multi-omic profiling is a promising approach to comprehensively analyze transcript-omics, prote-omics and metabol-omics on the same tissue section while preserving information about the spatial organization of cells. Integration of these spatial studies allows for inferring the consequences of complex cell-cell communication underlying tumor infiltration. We hypothesize that the glioma edge is enriched with pro-infiltration ligands-receptors driving tumor infiltration. Strategies that disrupt these ligand-receptor networks may suppress glioma infiltration and improve clinical outcomes. METHODS We utilized a glioma tissue micro-array (TMA) to establish the neoplastic and non-neoplastic heterogeneity in the GBM infiltration edge. Each TMA slide consists of pathologist annotated tumor core and edge samples of Glioblastoma (IDH Wildtype, WHO Grade 4; n=10), Diffuse Astrocytoma (IDH Mutant, WHO Grade 3; n=3), Oligodendroglioma (IDH mutant, WHO Grade 2; n=5) and 2 non-brain control samples. We performed spatial multi-omics profiling on adjacent sections of the TMA using 10x Xenium spatial transcriptomics, imaging mass cytometry (IMC) and mass spectrometry imaging (MSI). Integration, visualization, and quantification of the spatial data was done on VisioPharm. RESULTS In GBM, we identified candidate mRNA transcripts and proteins for ligands enriched at the infiltrating edge (compared to tumor core; p<0.0001) that correlated with a poor progression free survival (PFS; r2=0.22). These candidate ligands were also significantly enriched at the edge of oligodendroglioma WHO Grade 2 and astrocytoma WHO Grade 3. CONCLUSIONS Spatial multi-omics profiling on a TMA consisting of Glioma WHO grades 2-4 identified differentially expressed and targetable pro-tumor infiltration ligands associated with lower PFS in GBM. Functional studies to uncover the role of metabolites enriched at the glioma edge for the expression of the identified pro-infiltration ligands are ongoing.
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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.

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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.
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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.

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Fan, Rong, and Omer Bayraktar. "Special Issue: Spatial Omics." GEN Biotechnology 2, no. 2 (April 1, 2023): 61–62. http://dx.doi.org/10.1089/genbio.2023.29076.cfp2.

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Dissertations / Theses on the topic "Spatial omics"

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van, den Bruck David. "Spatial omics in neuronal cells - what goes where and why?" Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20232.

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Intrazelluläre Protein- und RNA-Lokalisation ist ein lebenswichtiger molekularer Mechanismus. Ihm unterliegen sowohl die äußere Gestaltung der Zellform, Zellagilität, zelluläre Differenzierung sowie die intra- sowie interzelluläre Kommunikation. Diverse Krankheiten werden mit Fehlfunktionen des intrazellulären Molekültransportes assoziiert und es existieren unzählige Beispiele für bekannte Wege des intrazellulären Protein- und RNA-Transportes. Allerdings ist die globale Komposition lokaler Protein- und RNA-Reservoirs bisher kaum wissenschaftlich erforscht worden. In dieser Studie beschreibe ich die Protein- sowie RNA-Kompositionen subzellulärer Fraktionen zweier neuronaler Zelltypen. Die Neuriten und Somata von Neuroblastoma-Zellen (N1E-115) und Ascl1 induzierten Neuronen (beides Mauszellen) wurden mechanisch voneinander separiert und mittels RNA-Sequenzierung und Massenspektrometrie auf ihre Bestandteile untersucht. Die Verteilung von mRNAs korreliert signifikant mit der Verteilung der entsprechenden Proteine in Ascl1-iNs während in der Neuroblastoma Zelllinie N1E-115 kein solcher Trend nachgewiesen werden konnte. Der Vergleich zu Datensätzen von anderen Zellsystemen und Methoden zeigt, dass das lokale Proteom sowie das lokale Transkriptome und Translatome stark Zelltyp spezifisch ist. Um den Einfluss lokaler Proteinbiosynthese auf die Komposition subzellulärer Proteinpools zu erheben, habe ich die Lokalisation neu synthetisierter Proteine untersucht. Es scheint, als sei die RNA-Lokalisation und lokale Translation von hoher Relevanz für die Protein-Lokalisation in diesen stark polarisierten Zellsystemen. Des Weiteren stelle ich eine Methode vor, um de novo „zip codes“ in diesen neuronalen Zellsystemen zu identifizieren. Diese könnte ein elementar wichtiger Schritt sein, um Fehlfunktionen im interzellulären Molekültransport zu verstehen.
Intracellular protein and RNA localization is one of the mayor players in the formation of cell shape, enabling cell agility, cellular differentiation and cell signaling. Various diseases are associated with malfunctions of intracellular molecule transport. There are many known pathways of how and why proteins and RNAs are transported within the cell and where they are located, though there is not much known about the global distribution of proteins and RNAs within the cell. In this study I apply a subcellular fractionation method coupled to multiple omics approaches to investigate the global distribution of mRNAs, noncoding RNAs and proteins in neuronal cells. Neurites and soma from mouse neuroblastoma cells (N1E-115) as well as from Ascl1 induced neurons (Ascl1-iNs) were isolated and the composition of the spatial proteome and transcriptome was examined. The localization of mRNAs correlates significantly with the localization of their corresponding protein products in Ascl1-iNs whereas it does not in the mouse neuroblastoma cell line N1E-115. Comparing these datasets with recently published data of other cell lines and methods it is clear, that the local proteome, transcriptome and translatome of neuronal cells is highly cell type specific. To investigate how spatial protein pools are established I analyzed local pools of newly synthesized proteins revealing that many proteins are synthesized on the spot. RNA localization therefore plays a crucial role in generating local protein pools in these highly polarized cell systems. Additionally, I propose a method to identify on a global scale de novo “zip codes” in these cell systems which would be a great step towards understanding malfunctions in molecule transport.
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Schmitz-Linneweber, Christian [Gutachter], Marina [Gutachter] Chekulaeva, and Matthias [Gutachter] Selbach. "Spatial omics in neuronal cells - what goes where and why? / Gutachter: Christian Schmitz-Linneweber, Marina Chekulaeva, Matthias Selbach." Berlin : Humboldt-Universität zu Berlin, 2019. http://d-nb.info/1200026233/34.

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Blampey, Quentin. "Deep learning and computational methods on single-cell and spatial data for precision medicine in oncology." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL116.

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La médecine de précision en oncologie a pour but de personnaliser les traitements en fonction des profils génétiques et moléculaires uniques des tumeurs des patients, et ce afin d'améliorer l'efficacité thérapeutique ou de minimiser les effets secondaires. À mesure que les avancées technologiques produisent des données de plus en plus précises sur le microenvironnement tumoral (TME), la complexité de ces données augmente également. Notamment, les données spatiales — un type récent et prometteur de données omiques — fournissent des informations moléculaires à la résolution de la cellule tout en conservant le contexte spatial des cellules au sein des tissus. Pour exploiter pleinement cette richesse et cette complexité, l'apprentissage profond émerge comme une approche capable de dépasser les limitations des approches traditionnelles. Ce manuscript détaille le développement de nouvelles méthodes de deep learning et computationnelles ayant pour but d'améliorer l'analyse des systèmes complexes des données single-cell et spatial. Trois outils sont décrits: (i) Scyan, pour l'annotation de types cellulaires en cytométrie, (ii) Sopa, une pipeline générale de preprocessing de données spatiales, et (iii) Novae, un modèle de fondation pour données spatiales. Ces méthodes sont appliqués à plusieurs projets de médecine de précision, approfondissant notre compréhension de la biologie du cancer et facilitant la découverte de nouveaux biomarqueurs et l'identification de cibles potentiellement actionnables pour la médecine de précision
Precision medicine in oncology customizes treatments based on the unique genetic and molecular profiles of patients' tumors, which is crucial for enhancing therapeutic efficacy and minimizing adverse effects. As technological advancements yield increasingly precise data about the tumor microenvironment (TME), the complexity of this data also grows. Notably, spatial data — a recent and promising type of omics data — provides molecular information at the single-cell level while maintaining the spatial context of cells within tissues. To fully exploit this rich and complex data, deep learning is emerging as a powerful approach that overcomes multiple limitations of traditional approaches. This manuscript details the development of new deep learning and computational methods to enhance our analysis of intricate systems like single-cell and spatial data. Three tools are introduced: (i) Scyan, for cell type annotation in cytometry, (ii) Sopa, a general pipeline for spatial omics, and (iii) Novae, a foundation model for spatial omics. These methods are applied to multiple precision medicine projects, exemplifying how they deepen our understanding of cancer biology, facilitating the discovery of new biomarkers and identifying potentially actionable targets for precision medicine
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DENTI, VANNA. "Development of multi-omic mass spectrometry imaging approaches to assist clinical investigations." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/365169.

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Con il termine di –omica spaziale si intende l’insieme di diverse tecniche che consentono di rilevare alterazioni significative delle biomolecole all’interno dei loro tessuti d’origine o delle strutture cellulari, permettendo quindi di integrare ed ampliare la comprensione dei cambiamenti biologici che si verificano in tessuti patologici complessi ed eterogenei, come il cancro. Tuttavia, per comprendere appieno la complessità e le dinamiche al di là delle condizioni patologiche, è necessario studiare e integrare diverse analisi molecolari, come quelle di lipidi e glicani, in modo da ottenere un’istantanea molecolare il più completa ed estesa possibile della malattia. Tra le tecniche di -omica spaziale, quella di desorbimento e ionizzazione laser assistiti da matrice (MALDI) abbinata alla spettrometria di massa imaging (MSI), permette lo studio della componente molecolare del tessuto patologico tramite un approccio multiplex, che permette di esaminare diverse centinaia di biomolecole in una singola analisi. Pertanto, l’analisi MALDI-MSI viene utilizzata per studi -omici spaziali di proteine, peptidi e N-glicani su campioni di tessuti clinici fissati in formalina e inclusi in paraffina (FFPE). Per quanto riguarda i lipidi, invece, questo tipo di analisi è sempre stato considerato poco efficace su campioni FFPE a causa della perdita di una grande quantità di contenuto lipidico durante le fasi di lavaggio con solventi organici, mentre i restanti lipidi resistenti ai solventi sono inaccessibili poiché trattenuti nei legami incrociati della formalina. In questi tre anni di dottorato, abbiamo sviluppato nuovi approcci MALDI-MSI per l'analisi spaziale multi-omica su campioni di tessuto clinico FFPE. Le prime tre pubblicazioni riportate in questa tesi si sono concentrate sullo sviluppo di protocolli MALDI-MSI per lipidi in campioni FFPE. In particolare, due di essi descrivono il metodo di preparazione del campione per la rilevazione di ioni di fosfolipidi carichi positivamente, principalmente fosfatidilcoline (PC), in campioni clinici di carcinoma renale a cellule chiare (ccRCC) e in un modello di xenotrapianto di cancro al seno. La terza pubblicazione riporta la possibilità di utilizzare ioni di fosfolipidi carichi negativamente, principalmente fosfatidilinositoli (PI), per definire firme lipidiche in grado di distinguere i gradi di tumore del colon-retto che presentano diverse quantità di linfociti infiltranti il tumore (TIL). Il lavoro finale propone un originale metodo MALDI-MSI multi-omico per l'analisi sequenziale di lipidi, N-glicani e peptidi triptici su una singola sezione FFPE. In particolare, il metodo è stato inizialmente implementato su replicati tecnici di cervello murino e successivamente utilizzato su campioni di ccRCC, come ulteriore prova, ottenendo una caratterizzazione più completa del tessuto tumorale grazie alla combinazione delle informazioni molecolari. Complessivamente, questi risultati aprono la strada a un nuovo approccio multi-omico spaziale basato sulla spettrometria di massa imaging (MSI) che è in grado di restituire un ritratto molecolare più ampio e più preciso della malattia.
The field of spatial omics defines the gathering of different techniques that allow the detection of significant alterations of biomolecules in the context of their native tissue or cellular structures. As such, they extend the landscape of biological changes occurring in complex and heterogeneous pathological tissues, such as cancer. However, additional molecular levels, such as lipids and glycans, must be studied to define a more comprehensive molecular snapshot of disease and fully understand the complexity and dynamics beyond pathological condition. Among the spatial-omics techniques, matrix-assisted laser desorption/ionisation (MALDI)-mass spectrometry imaging (MSI) offers a powerful insight into the chemical biology of pathological tissues in a multiplexed approach where several hundreds of biomolecules can be examined within a single experiment. Thus, MALDI-MSI has been readily employed for spatial omics studies of proteins, peptides and N-Glycans on clinical formalin-fixed paraffin-embedded (FFPE) tissue samples. Conversely, MALDI-MSI analysis of lipids has always been considered not feasible on FFPE samples due to the loss of a great amount of lipid content during washing steps with organic solvents, with the remaining solvent-resistant lipids being involved in the formalin cross-links. In this three-year thesis work, novel MALDI-MSI approaches for spatial multi-omics analysis on clinical FFPE tissue samples were developed. The first three publications reported in this thesis focused on the development of protocols for MALDI-MSI of lipids in FFPE samples. In particular, two of them describe a sample preparation method for the detection of positively charged phospholipids ions, mainly phosphatidylcholines (PCs), in clinical clear cell Renal Cell Carcinoma (ccRCC) samples and in a xenograft model of breast cancer. The third publication reports the possibility to use negatively charged phospholipids ions, mainly phosphatidylinositols (PIs), to define lipid signatures able to distinguish colorectal cancers with different amount of tumour infiltrating lymphocytes (TILs). The final work proposes a unique multi-omic MALDI-MSI method for the sequential analysis of lipids, N-Glycans and tryptic peptides on a single FFPE section. Specifically, the method feasibility was first established on murine brain technical replicates. The method was consequently used on ccRCC samples, as a proof of concept, assessing a more comprehensive characterisation of the tumour tissue when combining the multi-level molecular information. Altogether, these findings pave the way for new MSI-based spatial multi-omics approach aiming at an extensive and more precise molecular portrait of disease.
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Devaux, Stéphanie. "Spatio-temporal studies of the spinal cord injury through OMICs and physiological approaches." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10073/document.

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Les lésions de la moelle épinière (LME) appartiennent aux troubles incurables du système nerveux central. Les symptômes cliniques sont la conséquence des modifications dégénératives liées principalement à une inflammation aiguë, à la démyélinisation des axones et la formation d’une cavité qui perturbe les voies axonales. Nous mimons la LME grâce à la technique de ballon compression au niveau thoracique Th8-9 chez le rat. Une première étude a montré une régionalisation des protéines sécrétées 3 jours après lésion avec au niveau rostral un profil neuroprotecteur et au niveau caudal un profil neuroinflammatoire et apoptotique. Une étude spatio-temporelle a ensuite été mené pour compléter ces premiers résultats. Nous avons mis en évidence une symétrie entre les segments rostral et caudal avec la présence de facteurs neurotrophiques mais également d’inhibiteur de croissance neuritique au niveau caudal (lectines et RhoA). Des immunoglobulines ont été identifiés et semblent être colocalisées avec certains neurones. L’utilisation de biomatériaux injectés au niveau de la lésion permet de combler la cavité mais aussi de servir de réseau pour une future croissance axonale. Les alginates ont la capacité de libérer des facteurs qui permettront de moduler l’inflammation et de promouvoir la repousse neuritique. Les premiers résultats concernant l’injection de l’inhibiteur de RhoA tendent à montrer une augmentation de la repousse neuritique et des vésicules synaptiques au sein de la lésion. L’ensemble de ces résultats ont clairement démontré une évolution spatio-temporelle du profil moléculaire et ont défini le segment caudal comme étant une potentielle cible de traitement
Spinal cord injury (SCI) belongs to incurable disorders of the CNS. Primary damage and axonal disruption are followed by progressive cascade of secondary deleterious reactions. Although axonal regeneration is initiated, it is quickly repressed due to severe inflammation, lack of trophic support and inhibitory environment. In a balloon-compression SCI rat model the secretomes of the lesion segment and adjacent segments 3 days after SCI were studied and a regionalization of inflammatory and neurotrophic response between the rostral and caudal segments was highlighted. These results were complemented with spatiotemporal study of SCI. Rostral and caudal segments have shown the ability to regenerate due to the presence of immune cells with an anti-inflammatory and neurotrophic phenotype. However, a time lag occurs between segments, with a caudal segment near the lesion expressing inflammatory and apoptotic phenotype. This segment appears to be a potential target for future treatment. Indeed, this segment shows the presence of lectins and RhoA proteins but also the presence of antibodies colocalized with neurons. Therapeutic strategies have focused on the inhibition of these factors in addition to the use of biomaterials. Alginates fill the cavity and create a network facilitating axonal regrowth and have the ability to release factors which would modulate inflammation and stimulate regeneration. These data established spatiotemporal evolution and indicate that we can initiate regenerative processes in the caudal segment if trophic factors are added
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Chang, Chih-Wei, and 張至為. "Spatially resolved omics via photoredox catalysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/57mpy4.

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Books on the topic "Spatial omics"

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Spatial Omics: Methods for Reconstructing the Spatial Heterogeneity of Biological Tissue. Elsevier Science & Technology, 2023.

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Mcgourty, Kieran. Spatial Omics: Methods for Reconstructing the Spatial Heterogeneity of Biological Tissue. Elsevier Science & Technology Books, 2023.

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Siegel, Tiffany Porta. MALDI Mass Spectrometry Imaging: From Fundamentals to Spatial Omics. Royal Society of Chemistry, The, 2021.

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Siegel, Tiffany Porta. MALDI Mass Spectrometry Imaging: From Fundamentals to Spatial Omics. Royal Society of Chemistry, The, 2021.

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Siegel, Tiffany Porta. MALDI Mass Spectrometry Imaging: From Fundamentals to Spatial Omics. Royal Society of Chemistry, The, 2021.

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Pineda, Jesús, and Nathalie Reyns, eds. Larval Transport in the Coastal Zone: Biological and Physical Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0011.

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Larval transport is fundamental to several ecological processes, yet it remains unresolved for the majority of systems. We define larval transport, and describe its components, namely, larval behavior and the physical transport mechanisms accounting for advection, diffusion, and their variability. We then discuss other relevant processes in larval transport, including swimming proficiency, larval duration, accumulation in propagating features, episodic larval transport, and patchiness and spatial variability in larval abundance. We address challenges and recent approaches associated with understanding larval transport, including autonomous sampling, imaging, -omics, and the exponential growth in the use of poorly tested numerical simulation models to examine larval transport and population connectivity. Thus, we discuss the promises and pitfalls of numerical modeling, concluding with recommendations on moving forward, including a need for more process-oriented understanding of the mechanisms of larval transport and the use of emergent technologies.
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Book chapters on the topic "Spatial omics"

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Schrod, Stefan, Niklas Lück, Robert Lohmayer, Stefan Solbrig, Tina Wipfler, Katherine H. Shutta, Marouen Ben Guebila, et al. "SpaCeNet: Spatial Cellular Networks from Omics Data." In Lecture Notes in Computer Science, 344–47. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-1-0716-3989-4_27.

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Guo, Pengfei, and Yanxiang Deng. "Spatial Omics: Navigating Neuroscience Research into the New Era." In Advances in Neurobiology, 133–49. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69188-1_6.

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Biradar, Shantagoud, Chandana Korrapati, Ramya J. Krishna, and Nagashri Nanjundeshwara. "Spatial Omics and Gene Circuits." In Advances in Medical Diagnosis, Treatment, and Care, 383–408. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7728-4.ch014.

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Spatial transcriptomics is a powerful tool in biomedical research that enables the analysis of gene expression patterns within their spatial context. By providing insights into the spatial organisation of gene expression, this technology can help address limitations in the design of synthetic gene circuits in a controlled manner. In cancer biology, combining spatial transcriptomics with other approaches allows researchers to gain a deeper understanding of the spatiotemporal dynamics of gene regulation. This method has significant potential for revealing tissue architecture and cellular heterogeneity, which complements the analysis of gene circuits. This study explores the advantages of spatial transcriptomics and gene circuit analysis, focusing on integrating these techniques to understand better the spatial and temporal regulation of genes within tissues.
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Ma, Yinxing. "CRISPR screening meets spatial omics: Opportunities and challenges." In Reference Module in Biomedical Sciences. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-443-14064-8.00023-0.

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Biradar, Shantagoud, Chaaya Suresh, Nagashri Nanjundeshwara, and Ramya Raghavan. "Spatially Variable Genes." In Advances in Medical Diagnosis, Treatment, and Care, 1–28. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7728-4.ch001.

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The clinical translation of spatial transcriptomics represents cancer diagnosis and therapy based on the role and heterogeneity of cancer-associated fibroblasts (CAFs) within the tumor microenvironment (TME). Recent developments in spatial transcriptomics have enabled a detailed characterization of the spatial organization and cellular interactions within tumors. The data integration, multi-omics approaches, along with developing standardized protocols is essential for effective clinical translation. The experimental selection regimes and factorial designs reveals novel insights into the biomarkers and prognostic value of CAFs. The incorporation of optogenetics in cancer therapy and advancements in bio-engineered gene circuits, cellular therapeutics and tissue engineering further underscores the potential to refine patient stratification and improve treatment responsiveness. By integrating spatial transcriptomics into clinical workflows, this work aims to advance personalized cancer therapies and cancer biology.
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Swargam, Sandeep, and Indu Kumari. "An Introduction to the Integration of Systems Biology and OMICS data for Animal Scientists." In Systems Biology, Bioinformatics and Livestock Science, 1–16. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165616123010006.

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Systems biology integrates the data of all the omics studies and provides the avenues to understand the biology of an organism at higher levels like at tissue, organ or organism level. In the last decade, studies of genomics, transcriptomics, proteomics and metabolomics have been carried out. Only a limited amount of this big data has been analyzed, which is mainly focused on the genotype (single nucleotide polymorphism) level like minor allele frequency, copy number variation and structural variants. The analysis in transcriptomics is limited to differentially expressed genes and their ontology. Proteomics is focused on virulent factors, proteins involved in the disease progression and immunomodulation. However, in the case of livestock animals, there is a need to develop pipelines for the analysis of the omics data. With the integration of omics data into systems biology studies, there is a need to develop algorithms to carry out gene interaction and protein interaction studies and to build interaction networks. The pathway analysis of a system requires the well-defined interacting hub and edges of the protein system of an organism. Developing AI-ML models for drug discovery is required to target the pathogens of livestock animals. In the present era, the research is moving towards single-cell sequencing of the cells and tissues to explore the genetic heterogeneity in the micro-environment of the tissue and spatial biology of the tissue. This chapter will introduce the reader to different aspects of omics technology and its role in systems biology for better livestock management.
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Conference papers on the topic "Spatial omics"

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Wang, Bo, Wei Liu, Jiawei Luo, Xiangtao Chen, and Chee Keong Kwoh. "SMMGCL: a novel multi-level graph contrastive learning framework for integrating spatial multi-omics data." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1213–18. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822097.

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Martinez Martinez, Damian C., and Margarita S. Narducci. "Spatial Variation Prediction and Mapping of Soil Temperature." In 2020 Virtual Symposium in Plant Omics Sciences (OMICAS). IEEE, 2020. http://dx.doi.org/10.1109/omicas52284.2020.9535656.

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Baker, Ethan, Aaron Mayer, and Alexandro E. Trevino. "899 emObject: domain specific data abstraction for spatial omics." In SITC 38th Annual Meeting (SITC 2023) Abstracts. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/jitc-2023-sitc2023.0899.

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"How to integrate Spatial Omics techniques in your lab/core facility." In European Light Microscopy Initiative 2024. Royal Microscopical Society, 2024. http://dx.doi.org/10.22443/rms.elmi2024.30.

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Nilges, Benedikt S., Paul Kießling, Mar MMuniz Moreno, Niklas Klümper, Markus Eckstein, and Christoph Kuppe. "214 Decoding ADC-response in urothelial cancer with spatial multi-omics." In SITC 39th Annual Meeting (SITC 2024) Abstracts, A246. BMJ Publishing Group Ltd, 2024. http://dx.doi.org/10.1136/jitc-2024-sitc2024.0214.

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Shepherd, Douglas. "Spatial '-omics' in large samples using high numerical aperture oblique plane microscopy." In Virtual 12th Light Sheet Fluorescence Microscopy Conference 2020. Royal Microscopical Society, 2020. http://dx.doi.org/10.22443/rms.lsfm2020.42.

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Lokhande, Lavanya, Daniel Nilsson, Joana Rodrigues, May Hassan, Lina Olsson, Anna Porwit, Anna S. Gerdtsson, Mats Jerkeman, and Sara Ek. "1480 Spatially resolved T-cell microenvironment in mantle cell lymphoma using combined image analysis and spatial omics." In SITC 38th Annual Meeting (SITC 2023) Abstracts. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/jitc-2023-sitc2023.1480.

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Eng, Christine L., Joe P. Yeong, Andy Nguyen, Amanda Y. Guo, Brenda Tay, Mei Mei Chang, Sherlly Lim, et al. "Abstract 3872: Spatial and multi-omics characterization of the tumor microenvironment in colorectal cancer." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-3872.

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Elston, Katherine, Jessica Runyon, Vijay Baichwal, Arne Christians, Weston Stauffer, Analise Leddy, and Savannah Santoro. "1474 Investigating the molecular architecture of triple positive breast cancer samples with spatial omics technologies." In SITC 38th Annual Meeting (SITC 2023) Abstracts. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/jitc-2023-sitc2023.1474.

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Azher, Zarif L., Michael Fatemi, Yunrui Lu, Gokul Srinivasan, Alos B. Diallo, Brock C. Christensen, Lucas A. Salas, et al. "Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis." In Pacific Symposium on Biocomputing 2024. WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811286421_0036.

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Reports on the topic "Spatial omics"

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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.
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Pokrzywinski, Kaytee, Kaitlin Volk, Taylor Rycroft, Susie Wood, Tim Davis, and Jim Lazorchak. Aligning research and monitoring priorities for benthic cyanobacteria and cyanotoxins : a workshop summary. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41680.

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In 2018, the US Army Engineer Research and Development Center partnered with the US Army Corps of Engineers–Buffalo District, the US Environmental Protection Agency, Bowling Green State University, and the Cawthron Institute to host a workshop focused on benthic and sediment-associated cyanobacteria and cyanotoxins, particularly in the context of harmful algal blooms (HAB). Technical sessions on the ecology of benthic cyanobacteria in lakes and rivers; monitoring of cyanobacteria and cyanotoxins; detection of benthic and sediment-bound cyanotoxins; and the fate, transport, and health risks of cyanobacteria and their associated toxins were presented. Research summaries included the buoyancy and dispersal of benthic freshwater cyanobacteria mats, the fate and quantification of cyanotoxins in lake sediments, and spatial and temporal variation of toxins in streams. In addition, summaries of remote sensing methods, omic techniques, and field sampling techniques were presented. Critical research gaps identified from this workshop include (1) ecology of benthic cyanobacteria, (2) identity, fate, transport, and risk of cyanotoxins produced by benthic cyanobacteria, (3) standardized sampling and analysis protocols, and (4) increased technical cooperation between government, academia, industry, nonprofit organizations, and other stakeholders. Conclusions from this workshop can inform monitoring and management efforts for benthic cyanobacteria and their associated toxins.
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