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Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024; 16:243-260. [PMID: 38374297 DOI: 10.1007/s12539-024-00603-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhenqi Xiong
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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2
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Pang JMB, Byrne DJ, Bergin ART, Caramia F, Loi S, Gorringe KL, Fox SB. Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology. Histopathology 2024; 84:577-586. [PMID: 37991396 DOI: 10.1111/his.15093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/23/2023]
Abstract
In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.
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Affiliation(s)
- Jia-Min B Pang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - David J Byrne
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Alice R T Bergin
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Franco Caramia
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sherene Loi
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Kylie L Gorringe
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen B Fox
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
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3
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Bressan D, Battistoni G, Hannon GJ. The dawn of spatial omics. Science 2023; 381:eabq4964. [PMID: 37535749 PMCID: PMC7614974 DOI: 10.1126/science.abq4964] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/30/2023] [Indexed: 08/05/2023]
Abstract
Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.
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Affiliation(s)
- Dario Bressan
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Giorgia Battistoni
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Gregory J. Hannon
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
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Fenton KA, Pedersen HL. Advanced methods and novel biomarkers in autoimmune diseases ‑ a review of the recent years progress in systemic lupus erythematosus. Front Med (Lausanne) 2023; 10:1183535. [PMID: 37425332 PMCID: PMC10326284 DOI: 10.3389/fmed.2023.1183535] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023] Open
Abstract
There are several autoimmune and rheumatic diseases affecting different organs of the human body. Multiple sclerosis (MS) mainly affects brain, rheumatoid arthritis (RA) mainly affects joints, Type 1 diabetes (T1D) mainly affects pancreas, Sjogren's syndrome (SS) mainly affects salivary glands, while systemic lupus erythematosus (SLE) affects almost every organ of the body. Autoimmune diseases are characterized by production of autoantibodies, activation of immune cells, increased expression of pro-inflammatory cytokines, and activation of type I interferons. Despite improvements in treatments and diagnostic tools, the time it takes for the patients to be diagnosed is too long, and the main treatment for these diseases is still non-specific anti-inflammatory drugs. Thus, there is an urgent need for better biomarkers, as well as tailored, personalized treatment. This review focus on SLE and the organs affected in this disease. We have used the results from various rheumatic and autoimmune diseases and the organs involved with an aim to identify advanced methods and possible biomarkers to be utilized in the diagnosis of SLE, disease monitoring, and response to treatment.
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Affiliation(s)
- Kristin Andreassen Fenton
- UiT The Arctic University of Norway, Tromsø, Norway
- Centre of Clinical Research and Education, University Hospital of North Norway, Tromsø, Norway
| | - Hege Lynum Pedersen
- UiT The Arctic University of Norway, Tromsø, Norway
- Centre of Clinical Research and Education, University Hospital of North Norway, Tromsø, Norway
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5
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Heydari AA, Sindi SS. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing. BIOPHYSICS REVIEWS 2023; 4:011306. [PMID: 38505815 PMCID: PMC10903438 DOI: 10.1063/5.0091135] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 12/19/2022] [Indexed: 03/21/2024]
Abstract
Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.
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Single-cell transcriptomic analysis identifies murine heart molecular features at embryonic and neonatal stages. Nat Commun 2022; 13:7960. [PMID: 36575170 PMCID: PMC9794824 DOI: 10.1038/s41467-022-35691-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 12/19/2022] [Indexed: 12/28/2022] Open
Abstract
Heart development is a continuous process involving significant remodeling during embryogenesis and neonatal stages. To date, several groups have used single-cell sequencing to characterize the heart transcriptomes but failed to capture the progression of heart development at most stages. This has left gaps in understanding the contribution of each cell type across cardiac development. Here, we report the transcriptional profile of the murine heart from early embryogenesis to late neonatal stages. Through further analysis of this dataset, we identify several transcriptional features. We identify gene expression modules enriched at early embryonic and neonatal stages; multiple cell types in the left and right atriums are transcriptionally distinct at neonatal stages; many congenital heart defect-associated genes have cell type-specific expression; stage-unique ligand-receptor interactions are mostly between epicardial cells and other cell types at neonatal stages; and mutants of epicardium-expressed genes Wt1 and Tbx18 have different heart defects. Assessment of this dataset serves as an invaluable source of information for studies of heart development.
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Young LV, Wakelin G, Cameron AWR, Springer SA, Ross JP, Wolters G, Murphy JP, Arsenault MG, Ng S, Collao N, De Lisio M, Ljubicic V, Johnston APW. Muscle injury induces a transient senescence-like state that is required for myofiber growth during muscle regeneration. FASEB J 2022; 36:e22587. [PMID: 36190443 DOI: 10.1096/fj.202200289rr] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/14/2022] [Accepted: 09/21/2022] [Indexed: 11/11/2022]
Abstract
Cellular senescence is the irreversible arrest of normally dividing cells and is driven by the cell cycle inhibitors Cdkn2a, Cdkn1a, and Trp53. Senescent cells are implicated in chronic diseases and tissue repair through their increased secretion of pro-inflammatory factors known as the senescence-associated secretory phenotype (SASP). Here, we use spatial transcriptomics and single-cell RNA sequencing (scRNAseq) to demonstrate that cells displaying senescent characteristics are "transiently" present within regenerating skeletal muscle and within the muscles of D2-mdx mice, a model of Muscular Dystrophy. Following injury, multiple cell types including macrophages and fibrog-adipogenic progenitors (FAPs) upregulate senescent features such as senescence pathway genes, SASP factors, and senescence-associated beta-gal (SA-β-gal) activity. Importantly, when these cells were removed with ABT-263, a senolytic compound, satellite cells are reduced, and muscle fibers were impaired in growth and myonuclear accretion. These results highlight that an "acute" senescent phenotype facilitates regeneration similar to skin and neonatal myocardium.
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Affiliation(s)
- Laura V Young
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Griffen Wakelin
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Alasdair W R Cameron
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Stevan A Springer
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Joel P Ross
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Grant Wolters
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - J Patrick Murphy
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Michel G Arsenault
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
| | - Sean Ng
- Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada
| | - Nicolás Collao
- School of Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada
| | - Michael De Lisio
- School of Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada
| | - Vladimir Ljubicic
- Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada
| | - Adam P W Johnston
- Department of Applied Human Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada.,Department of Biomedical Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada.,Dapartment of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
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Cuevas-Diaz Duran R, González-Orozco JC, Velasco I, Wu JQ. Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases. Front Cell Dev Biol 2022; 10:884748. [PMID: 36353512 PMCID: PMC9637968 DOI: 10.3389/fcell.2022.884748] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 10/06/2022] [Indexed: 08/10/2023] Open
Abstract
Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.
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Affiliation(s)
| | | | - Iván Velasco
- Instituto de Fisiología Celular—Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Laboratorio de Reprogramación Celular, Instituto Nacional de Neurología y Neurocirugía “Manuel Velasco Suárez”, Mexico City, Mexico
| | - Jia Qian Wu
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, United States
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States
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Kleino I, Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20:4870-4884. [PMID: 36147664 PMCID: PMC9464853 DOI: 10.1016/j.csbj.2022.08.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022] Open
Abstract
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
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Key Words
- AOI, area of illumination
- BICCN, Brain Initiative Cell Census Network
- BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
- Baysor, Bayesian Segmentation of Spatial Transcriptomics Data
- BinSpect, Binary Spatial Extraction
- CCC, cell–cell communication
- CCI, cell–cell interactions
- CNV, copy-number variation
- Computational biology
- DSP, digital spatial profiling
- DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing
- FA, factor analysis
- FFPE, formalin-fixed, paraffin-embedded
- FISH, fluorescence in situ hybridization
- FISSEQ, fluorescence in situ sequencing of RNA
- FOV, Field of view
- GRNs, gene regulation networks
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- HDST, high definition spatial transcriptomics
- HMRF, hidden Markov random field
- ICG, interaction changed genes
- ISH, in situ hybridization
- ISS, in situ sequencing
- JSTA, Joint cell segmentation and cell type annotation
- KNN, k-nearest neighbor
- LCM, Laser Capture Microdissection
- LCM-seq, laser capture microdissection coupled with RNA sequencing
- LOH, loss of heterozygosity analysis
- MC, Molecular Cartography
- MERFISH, multiplexed error-robust FISH
- NMF (NNMF), Non-negative matrix factorization
- PCA, Principal Component Analysis
- PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing
- PL-lig, padlock ligation
- QC, quality control
- RNAseq, RNA sequencing
- ROI, region of interest
- SCENIC, Single-Cell rEgulatory Network Inference and Clustering
- SME, Spatial Morphological gene Expression normalization
- SPATA, SPAtial Transcriptomic Analysis
- ST Pipeline, Spatial Transcriptomics Pipeline
- ST, Spatial transcriptomics
- STARmap, spatially-resolved transcript amplicon readout mapping
- Single-cell analysis
- Spatial data analysis frameworks
- Spatial deconvolution
- Spatial transcriptomics
- TIVA, Transcriptome in Vivo Analysis
- TMA, tissue microarray
- TME, tumor micro environment
- UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
- UMI, unique molecular identifier
- ZipSeq, zipcoded sequencing.
- scRNA-seq, single-cell RNA sequencing
- scvi-tools, single-cell variational inference tools
- seqFISH, sequential fluorescence in situ hybridization
- sequ-smFISH, sequential single-molecule fluorescent in situ hybridization
- smFISH, single molecule FISH
- t-SNE, t-distributed stochastic neighbor embedding
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Affiliation(s)
- Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Paulina Frolovaitė
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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Baratta AM, Brandner AJ, Plasil SL, Rice RC, Farris SP. Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function. Front Mol Neurosci 2022; 15:905328. [PMID: 35813067 PMCID: PMC9259865 DOI: 10.3389/fnmol.2022.905328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.
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Affiliation(s)
- Annalisa M. Baratta
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Adam J. Brandner
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sonja L. Plasil
- Department of Pharmacology & Chemical Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Rachel C. Rice
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sean P. Farris
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- *Correspondence: Sean P. Farris,
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Marshall JL, Noel T, Wang QS, Chen H, Murray E, Subramanian A, Vernon KA, Bazua-Valenti S, Liguori K, Keller K, Stickels RR, McBean B, Heneghan RM, Weins A, Macosko EZ, Chen F, Greka A. High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. iScience 2022; 25:104097. [PMID: 35372810 PMCID: PMC8971939 DOI: 10.1016/j.isci.2022.104097] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022] Open
Abstract
High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods. A cell neighborhood around LYVE1+ macrophages was discovered in human kidneys The blood pressure regulating apparatus was re-organized in a diabetic mouse model Cell neighborhoods around Trem2+ macrophages were found in a model of proteinopathy A 77 gene signature associated with the UPR was defined in a model of proteinopathy
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Minciuna CE, Tanase M, Manuc TE, Tudor S, Herlea V, Dragomir MP, Calin GA, Vasilescu C. The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers. Comput Struct Biotechnol J 2022; 20:5065-5075. [PMID: 36187924 PMCID: PMC9489806 DOI: 10.1016/j.csbj.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/07/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies.
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