1
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Crouch SA, Krause J, Dandekar T, Breitenbach T. DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research. Comput Struct Biotechnol J 2024; 23:1755-1772. [PMID: 38707537 PMCID: PMC11068525 DOI: 10.1016/j.csbj.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/07/2024] Open
Abstract
Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.
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Affiliation(s)
| | | | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland 97074, Würzburg, Germany
| | - Tim Breitenbach
- Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland 97074, Würzburg, Germany
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2
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Gutierrez Reyes CD, Alejo-Jacuinde G, Perez Sanchez B, Chavez Reyes J, Onigbinde S, Mogut D, Hernández-Jasso I, Calderón-Vallejo D, Quintanar JL, Mechref Y. Multi Omics Applications in Biological Systems. Curr Issues Mol Biol 2024; 46:5777-5793. [PMID: 38921016 PMCID: PMC11202207 DOI: 10.3390/cimb46060345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.
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Affiliation(s)
| | - Gerardo Alejo-Jacuinde
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance (IGCAST), Texas Tech University, Lubbock, TX 79409, USA; (G.A.-J.); (B.P.S.)
| | - Benjamin Perez Sanchez
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance (IGCAST), Texas Tech University, Lubbock, TX 79409, USA; (G.A.-J.); (B.P.S.)
| | - Jesus Chavez Reyes
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - Sherifdeen Onigbinde
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA;
| | - Damir Mogut
- Department of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland;
| | - Irma Hernández-Jasso
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - Denisse Calderón-Vallejo
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - J. Luis Quintanar
- Center of Basic Sciences, Department of Physiology and Pharmacology, Autonomous University of Aguascalientes, Aguascalientes 20392, Mexico; (J.C.R.); (I.H.-J.); (D.C.-V.); (J.L.Q.)
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA;
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3
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Procopio N, Bonicelli A. From flesh to bones: Multi-omics approaches in forensic science. Proteomics 2024; 24:e2200335. [PMID: 38683823 DOI: 10.1002/pmic.202200335] [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: 10/28/2023] [Revised: 03/12/2024] [Accepted: 03/26/2024] [Indexed: 05/02/2024]
Abstract
Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.
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Affiliation(s)
- Noemi Procopio
- Research Centre for Field Archaeology and Experimental Taphonomy, School of Law and Policing, University of Central Lancashire, Preston, UK
| | - Andrea Bonicelli
- Research Centre for Field Archaeology and Experimental Taphonomy, School of Law and Policing, University of Central Lancashire, Preston, UK
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4
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Kumari P, Kaur M, Dindhoria K, Ashford B, Amarasinghe SL, Thind AS. Advances in long-read single-cell transcriptomics. Hum Genet 2024:10.1007/s00439-024-02678-x. [PMID: 38787419 DOI: 10.1007/s00439-024-02678-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.
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Affiliation(s)
- Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Manmeet Kaur
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Bruce Ashford
- Illawarra Shoalhaven Local Health District (ISLHD), NSW Health, Wollongong, NSW, Australia
| | - Shanika L Amarasinghe
- Monash Biomedical Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
- Walter and Eliza Hall Institute of Medical Research, 1G, Royal Parade, Parkville, VIC, 3025, Australia
| | - Amarinder Singh Thind
- Illawarra Shoalhaven Local Health District (ISLHD), NSW Health, Wollongong, NSW, Australia.
- The School of Chemistry and Molecular Bioscience (SCMB), University of Wollongong, Loftus St, Wollongong, NSW, 2500, Australia.
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5
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Wang GF, Shen L. Cauchy hyper-graph Laplacian nonnegative matrix factorization for single-cell RNA-sequencing data analysis. BMC Bioinformatics 2024; 25:169. [PMID: 38684942 PMCID: PMC11059750 DOI: 10.1186/s12859-024-05797-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/24/2024] [Indexed: 05/02/2024] Open
Abstract
Many important biological facts have been found as single-cell RNA sequencing (scRNA-seq) technology has advanced. With the use of this technology, it is now possible to investigate the connections among individual cells, genes, and illnesses. For the analysis of single-cell data, clustering is frequently used. Nevertheless, biological data usually contain a large amount of noise data, and traditional clustering methods are sensitive to noise. However, acquiring higher-order spatial information from the data alone is insufficient. As a result, getting trustworthy clustering findings is challenging. We propose the Cauchy hyper-graph Laplacian non-negative matrix factorization (CHLNMF) as a unique approach to address these issues. In CHLNMF, we replace the measurement based on Euclidean distance in the conventional non-negative matrix factorization (NMF), which can lessen the influence of noise, with the Cauchy loss function (CLF). The model also incorporates the hyper-graph constraint, which takes into account the high-order link among the samples. The CHLNMF model's best solution is then discovered using a half-quadratic optimization approach. Finally, using seven scRNA-seq datasets, we contrast the CHLNMF technique with the other nine top methods. The validity of our technique was established by analysis of the experimental outcomes.
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Affiliation(s)
- Gao-Fei Wang
- School of Computer Science, Qufu Normal University, Rizhao, 276826, Shandong, China.
| | - Longying Shen
- School of Computer Science, Qufu Normal University, Rizhao, 276826, Shandong, China
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6
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Yuan X, Wang H, Sun Z, Zhou C, Chu SC, Bu J, Shen N. Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data. CELL REPORTS METHODS 2024; 4:100733. [PMID: 38503288 PMCID: PMC10985232 DOI: 10.1016/j.crmeth.2024.100733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/15/2024] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
Here, we present Anchored-fusion, a highly sensitive fusion gene detection tool. It anchors a gene of interest, which often involves driver fusion events, and recovers non-unique matches of short-read sequences that are typically filtered out by conventional algorithms. In addition, Anchored-fusion contains a module based on a deep learning hierarchical structure that incorporates self-distillation learning (hierarchical view learning and distillation [HVLD]), which effectively filters out false positive chimeric fragments generated during sequencing while maintaining true fusion genes. Anchored-fusion enables highly sensitive detection of fusion genes, thus allowing for application in cases with low sequencing depths. We benchmark Anchored-fusion under various conditions and found it outperformed other tools in detecting fusion events in simulated data, bulk RNA sequencing (bRNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Our results demonstrate that Anchored-fusion can be a useful tool for fusion detection tasks in clinically relevant RNA-seq data and can be applied to investigate intratumor heterogeneity in scRNA-seq data.
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Affiliation(s)
- Xilu Yuan
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
| | - Haishuai Wang
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
| | - Zhongquan Sun
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunpeng Zhou
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
| | - Simon Chong Chu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jiajun Bu
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
| | - Ning Shen
- Liangzhu Laboratory, Zhejiang University, Hangzhou, China.
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7
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Ghaffari S, Saleh M, Akbari B, Ramezani F, Mirzaei HR. Applications of single-cell omics for chimeric antigen receptor T cell therapy. Immunology 2024; 171:339-364. [PMID: 38009707 DOI: 10.1111/imm.13720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 11/13/2023] [Indexed: 11/29/2023] Open
Abstract
Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single-cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single-cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single-cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single-cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi-omics of CAR T cell therapy.
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Affiliation(s)
- Sasan Ghaffari
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Mahshid Saleh
- Wisconsin National Primate Research Center, University of Wisconsin Graduate School, Madison, Wisconsin, USA
| | - Behnia Akbari
- Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Faezeh Ramezani
- Department of Medical Biotechnology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Medical Laboratory Sciences, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamid Reza Mirzaei
- Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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8
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Martínez-Torres D, Maldonado V, Pérez-Gallardo C, Yañez R, Candia V, Kalaidzidis Y, Zerial M, Morales-Navarrete H, Segovia-Miranda F. Phenotypic characterization of liver tissue heterogeneity through a next-generation 3D single-cell atlas. Sci Rep 2024; 14:2823. [PMID: 38307948 PMCID: PMC10837128 DOI: 10.1038/s41598-024-53309-4] [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: 08/26/2023] [Accepted: 01/30/2024] [Indexed: 02/04/2024] Open
Abstract
Three-dimensional (3D) geometrical models are potent tools for quantifying complex tissue features and exploring structure-function relationships. However, these models are generally incomplete due to experimental limitations in acquiring multiple (> 4) fluorescent channels in thick tissue sections simultaneously. Indeed, predictive geometrical and functional models of the liver have been restricted to few tissue and cellular components, excluding important cellular populations such as hepatic stellate cells (HSCs) and Kupffer cells (KCs). Here, we combined deep-tissue immunostaining, multiphoton microscopy, deep-learning techniques, and 3D image processing to computationally expand the number of simultaneously reconstructed tissue structures. We then generated a spatial single-cell atlas of hepatic architecture (Hep3D), including all main tissue and cellular components at different stages of post-natal development in mice. We used Hep3D to quantitatively study 1) hepatic morphodynamics from early post-natal development to adulthood, and 2) the effect on the liver's overall structure when changing the hepatic environment after removing KCs. In addition to a complete description of bile canaliculi and sinusoidal network remodeling, our analysis uncovered unexpected spatiotemporal patterns of non-parenchymal cells and hepatocytes differing in size, number of nuclei, and DNA content. Surprisingly, we found that the specific depletion of KCs results in morphological changes in hepatocytes and HSCs. These findings reveal novel characteristics of liver heterogeneity and have important implications for both the structural organization of liver tissue and its function. Our next-gen 3D single-cell atlas is a powerful tool to understand liver tissue architecture, opening up avenues for in-depth investigations into tissue structure across both normal and pathological conditions.
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Affiliation(s)
- Dilan Martínez-Torres
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Valentina Maldonado
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Cristian Pérez-Gallardo
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Rodrigo Yañez
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Valeria Candia
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Yannis Kalaidzidis
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Marino Zerial
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Hernán Morales-Navarrete
- Department of Systems Biology of Development, University of Konstanz, Konstanz, Germany.
- Facultad de Ciencias Técnicas, Universidad Internacional Del Ecuador UIDE, Quito, Ecuador.
| | - Fabián Segovia-Miranda
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile.
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile.
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9
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Bui BN, Ardisasmita AI, Kuijk E, Altmäe S, Steba G, Mackens S, Fuchs S, Broekmans F, Nieuwenhuis E. An unbiased approach of molecular characterization of the endometrium: toward defining endometrial-based infertility. Hum Reprod 2024; 39:275-281. [PMID: 38099857 PMCID: PMC10833067 DOI: 10.1093/humrep/dead257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 10/01/2023] [Indexed: 02/02/2024] Open
Abstract
Infertility is a complex condition affecting millions of couples worldwide. The current definition of infertility, based on clinical criteria, fails to account for the molecular and cellular changes that may occur during the development of infertility. Recent advancements in sequencing technology and single-cell analysis offer new opportunities to gain a deeper understanding of these changes. The endometrium has a potential role in infertility and has been extensively studied to identify gene expression profiles associated with (impaired) endometrial receptivity. However, limited overlap among studies hampers the identification of relevant downstream pathways that could play a role in the development of endometrial-related infertility. To address these challenges, we propose sequencing the endometrial transcriptome of healthy and infertile women at the single-cell level to consistently identify molecular signatures. Establishing consensus on physiological patterns in endometrial samples can aid in identifying deviations in infertile patients. A similar strategy has been used with great success in cancer research. However, large collaborative initiatives, international uniform protocols of sample collection and processing are crucial to ensure reliability and reproducibility. Overall, the proposed approach holds promise for an objective and accurate classification of endometrial-based infertility and has the potential to improve diagnosis and treatment outcomes.
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Affiliation(s)
- Bich Ngoc Bui
- Department of Gynaecology and Reproductive Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Ewart Kuijk
- Department of Pediatric Gastroenterology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Signe Altmäe
- Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain
| | - Gaby Steba
- Department of Gynaecology and Reproductive Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Shari Mackens
- Brussels IVF, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sabine Fuchs
- Department of Metabolic Diseases, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Frank Broekmans
- Department of Gynaecology and Reproductive Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Centre for Infertility Care, Dijklander Ziekenhuis, Purmerend, The Netherlands
| | - Edward Nieuwenhuis
- Department of Pediatric Gastroenterology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Science, University College Roosevelt, Middelburg, The Netherlands
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Bhattachan P, Jeschke MG. SINGLE-CELL TRANSCRIPTOME ANALYSIS IN HEALTH AND DISEASE. Shock 2024; 61:19-27. [PMID: 37962963 PMCID: PMC10883422 DOI: 10.1097/shk.0000000000002274] [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] [Indexed: 11/16/2023]
Abstract
ABSTRACT The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights on the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. The prime advantage of single-cell transcriptome analysis is the detection of stochastic nature of gene expression of the cell in tissue. Moreover, the availability of multiple platforms for the single-cell transcriptome has broadened its approaches to using cells of different sizes and shapes, including the capture of short or full-length transcripts, which is helpful in the analysis of challenging biological samples. And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. Downstream analysis such as differential gene expression analysis, gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis to further examine the biology of different cell types. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.
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11
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Sammeth M, Mudra S, Bialdiga S, Hartmannsberger B, Kramer S, Rittner H. Comparative Methods for Demystifying Spatial Transcriptomics. Methods Mol Biol 2024; 2802:515-546. [PMID: 38819570 DOI: 10.1007/978-1-0716-3838-5_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Spatial Transcriptomics (ST), coined as the term for parallel RNA-Seq on cell populations ordered spatially on a histological tissue section, has recently become increasingly popular, especially in experiments where microfluidics-based single-cell sequencing fails, such as assays on neurons. ST platforms, like the 10x Visium technology investigated herein, therefore produce in a single experiment simultaneously thousands of RNA readouts, captured by an array of micrometer scale spots under the histological section. Therefore, a central challenge of analyzing ST experiments consists of analyzing the gene expression morphology of all spots to delineate clusters of similar cell mixtures, which are then compared to each other to identify up- or down-regulated marker genes. Moreover, another level of complexity in ST experiments, compared to traditional RNA-Seq, is imposed by staining the tissue section with protein markers of cells or cell components to identify spots providing relevant information afterward. The corresponding microscopy images need to be analyzed in addition to the RNA-Seq read mappings on the reference genome and transcriptome sequences. Focusing on the software suite provided by the Visium platform manufacturer, we break down the ST analysis pipeline into its four essential steps-the image analysis, the read alignment, the gene quantification, and the spot clustering-and compare results obtained when using reads from different subsets of spots and/or when employing alternative genome or transcriptome references. Our comparative analyses demonstrate the impact of spot selection and the choice of genome/transcriptome references on the analysis results when employing the manufacturer's pipeline.
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Affiliation(s)
- Michael Sammeth
- Coburg University of Applied Sciences and Art, Department of Applied Sciences and Health, Coburg, Germany.
- University Hospital Würzburg, Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Würzburg, Germany.
| | - Susann Mudra
- Coburg University of Applied Sciences and Art, Department of Applied Sciences and Health, Coburg, Germany
| | - Sina Bialdiga
- Coburg University of Applied Sciences and Art, Department of Applied Sciences and Health, Coburg, Germany
| | - Beate Hartmannsberger
- University Hospital Würzburg, Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Centre for Interdisciplinary Pain Medicine, Würzburg, Germany
| | - Sofia Kramer
- University Hospital Würzburg, Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Centre for Interdisciplinary Pain Medicine, Würzburg, Germany
| | - Heike Rittner
- University Hospital Würzburg, Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Centre for Interdisciplinary Pain Medicine, Würzburg, Germany
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12
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Tan JK, Awuah WA, Roy S, Ferreira T, Ahluwalia A, Guggilapu S, Javed M, Asyura MMAZ, Adebusoye FT, Ramamoorthy K, Paoletti E, Abdul-Rahman T, Prykhodko O, Ovechkin D. Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review. Med Oncol 2023; 41:27. [PMID: 38129369 PMCID: PMC10739406 DOI: 10.1007/s12032-023-02260-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023]
Abstract
Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.
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Affiliation(s)
| | | | - Sakshi Roy
- School of Medicine, Queen's University Belfast, Belfast, UK
| | - Tomas Ferreira
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Saibaba Guggilapu
- Faculty of Medicine, Bangalore Medical College and Research Institute, Bengaluru, India
| | - Mahnoor Javed
- School of Medicine, The University of Nottingham, Nottingham, NG7 2UH, UK
| | | | | | | | - Emma Paoletti
- Faculty of Medicine, University of Manchester, Manchester, M13 9WJ, UK
| | | | - Olha Prykhodko
- Faculty of Medicine, Sumy State University, Sumy, Ukraine
| | - Denys Ovechkin
- Faculty of Medicine, Sumy State University, Sumy, Ukraine
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13
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Zhang C, Fang Y, Chen W, Chen Z, Zhang Y, Xie Y, Chen W, Xie Z, Guo M, Wang J, Tan C, Wang H, Tang C. Improving the RNA velocity approach with single-cell RNA lifecycle (nascent, mature and degrading RNAs) sequencing technologies. Nucleic Acids Res 2023; 51:e112. [PMID: 37941145 PMCID: PMC10711548 DOI: 10.1093/nar/gkad969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/27/2023] [Accepted: 10/14/2023] [Indexed: 11/10/2023] Open
Abstract
We presented an experimental method called FLOUR-seq, which combines BD Rhapsody and nanopore sequencing to detect the RNA lifecycle (including nascent, mature, and degrading RNAs) in cells. Additionally, we updated our HIT-scISOseq V2 to discover a more accurate RNA lifecycle using 10x Chromium and Pacbio sequencing. Most importantly, to explore how single-cell full-length RNA sequencing technologies could help improve the RNA velocity approach, we introduced a new algorithm called 'Region Velocity' to more accurately configure cellular RNA velocity. We applied this algorithm to study spermiogenesis and compared the performance of FLOUR-seq with Pacbio-based HIT-scISOseq V2. Our findings demonstrated that 'Region Velocity' is more suitable for analyzing single-cell full-length RNA data than traditional RNA velocity approaches. These novel methods could be useful for researchers looking to discover full-length RNAs in single cells and comprehensively monitor RNA lifecycle in cells.
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Affiliation(s)
| | | | - Weitian Chen
- BGI, Shenzhen 518000, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
| | | | - Ying Zhang
- Guangdong Provincial Reproductive Science Institute (Guangdong Provincial Fertility Hospital), Guangzhou, China; NHC Key Laboratory of Male Reproduction and Genetics, Guangzhou, China
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14
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Liu J, Zeng W, Kan S, Li M, Zheng R. CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification. Brief Bioinform 2023; 25:bbad475. [PMID: 38145950 PMCID: PMC10749894 DOI: 10.1093/bib/bbad475] [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: 10/07/2023] [Revised: 11/13/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023] Open
Abstract
Single cell sequencing technology has provided unprecedented opportunities for comprehensively deciphering cell heterogeneity. Nevertheless, the high dimensionality and intricate nature of cell heterogeneity have presented substantial challenges to computational methods. Numerous novel clustering methods have been proposed to address this issue. However, none of these methods achieve the consistently better performance under different biological scenarios. In this study, we developed CAKE, a novel and scalable self-supervised clustering method, which consists of a contrastive learning model with a mixture neighborhood augmentation for cell representation learning, and a self-Knowledge Distiller model for the refinement of clustering results. These designs provide more condensed and cluster-friendly cell representations and improve the clustering performance in term of accuracy and robustness. Furthermore, in addition to accurately identifying the major type cells, CAKE could also find more biologically meaningful cell subgroups and rare cell types. The comprehensive experiments on real single-cell RNA sequencing datasets demonstrated the superiority of CAKE in visualization and clustering over other comparison methods, and indicated its extensive application in the field of cell heterogeneity analysis. Contact: Ruiqing Zheng. (rqzheng@csu.edu.cn).
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Affiliation(s)
- Jin Liu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Weixing Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Shichao Kan
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
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15
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Dong M, Wang B, Wei J, de O Fonseca AH, Perry CJ, Frey A, Ouerghi F, Foxman EF, Ishizuka JJ, Dhodapkar RM, van Dijk D. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nat Methods 2023; 20:1769-1779. [PMID: 37919419 PMCID: PMC10630139 DOI: 10.1038/s41592-023-02040-5] [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: 11/14/2022] [Accepted: 09/08/2023] [Indexed: 11/04/2023]
Abstract
Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. Here we present a causal-inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (causal independent effect module attribution + optimal transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment-effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment-effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly generated datasets: (1) rhinovirus and cigarette-smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette-smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.
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Affiliation(s)
- Mingze Dong
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Bao Wang
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Jessica Wei
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | | | - Curtis J Perry
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Alexander Frey
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
- Department of Surgery, Yale School of Medicine, New Haven, CT, USA
| | - Feriel Ouerghi
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Ellen F Foxman
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
| | - Jeffrey J Ishizuka
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA.
| | - Rahul M Dhodapkar
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - David van Dijk
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
- Department of Internal Medicine (Cardiology), Yale School of Medicine, New Haven, CT, USA.
- Department of Computer Science, Yale University, New Haven, CT, USA.
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16
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Kadri MS, Singhania RR, Haldar D, Patel AK, Bhatia SK, Saratale G, Parameswaran B, Chang JS. Advances in Algomics technology: Application in wastewater treatment and biofuel production. BIORESOURCE TECHNOLOGY 2023; 387:129636. [PMID: 37544548 DOI: 10.1016/j.biortech.2023.129636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/08/2023]
Abstract
Advanced sustainable bioremediation is gaining importance with rising global pollution. This review examines microalgae's potential for sustainable bioremediation and process enhancement using multi-omics approaches. Recently, microalgae-bacterial consortia have emerged for synergistic nutrient removal, allowing complex metabolite exchanges. Advanced bioremediation requires effective consortium design or pure culture based on the treatment stage and specific roles. The strain potential must be screened using modern omics approaches aligning wastewater composition. The review highlights crucial research gaps in microalgal bioremediation. It discusses multi-omics advantages for understanding microalgal fitness concerning wastewater composition and facilitating the design of microalgal consortia based on bioremediation skills. Metagenomics enables strain identification, thereby monitoring microbial dynamics during the treatment process. Transcriptomics and metabolomics encourage the algal cell response toward nutrients and pollutants in wastewater. Multi-omics role is also summarized for product enhancement to make algal treatment sustainable and fit for sustainable development goals and growing circular bioeconomy scenario.
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Affiliation(s)
- Mohammad Sibtain Kadri
- Department of Marine Biotechnology and Resources, National Sun Yat-Sen University, Kaohsiung City 804201, Taiwan
| | - Reeta Rani Singhania
- Institute of Aquatic Science and Technology, National Kaohsiung University of Science and Technology, Kaohsiung City 81157, Taiwan; Centre for Energy and Environmental Sustainability, Lucknow 226 029, Uttar Pradesh, India
| | - Dibyajyoti Haldar
- Department of Biotechnology, Karunya Institute of Technology and Sciences, Coimbatore 641114, India
| | - Anil Kumar Patel
- Institute of Aquatic Science and Technology, National Kaohsiung University of Science and Technology, Kaohsiung City 81157, Taiwan; Centre for Energy and Environmental Sustainability, Lucknow 226 029, Uttar Pradesh, India.
| | - Shashi Kant Bhatia
- Department of Biological Engineering, College of Engineering, Konkuk University, Seoul 805029, Republic of Korea
| | - Ganesh Saratale
- Department of Food Science and Biotechnology, Dongguk University-Seoul, Ilsandong-gu, Goyang-si 10326, Republic of Korea
| | - Binod Parameswaran
- Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Trivandrum 695 019, Kerala, India
| | - Jo-Shu Chang
- Department of Chemical Engineering, National Cheng Kung University, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taiwan.
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17
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Pan J, Chang Z, Zhang X, Dong Q, Zhao H, Shi J, Wang G. Research progress of single-cell sequencing in tuberculosis. Front Immunol 2023; 14:1276194. [PMID: 37901241 PMCID: PMC10611525 DOI: 10.3389/fimmu.2023.1276194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.
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Affiliation(s)
| | | | | | | | | | - Jingwei Shi
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
| | - Guoqing Wang
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
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18
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Zyla J, Papiez A, Zhao J, Qu R, Li X, Kluger Y, Polanska J, Hatzis C, Pusztai L, Marczyk M. Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms. Comput Struct Biotechnol J 2023; 21:4663-4674. [PMID: 37841335 PMCID: PMC10568495 DOI: 10.1016/j.csbj.2023.09.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/17/2023] Open
Abstract
Recent advances in sample preparation and sequencing technology have made it possible to profile the transcriptomes of individual cells using single-cell RNA sequencing (scRNA-Seq). Compared to bulk RNA-Seq data, single-cell data often contain a higher percentage of zero reads, mainly due to lower sequencing depth per cell, which affects mostly measurements of low-expression genes. However, discrepancies between platforms are observed regardless of expression level. Using four paired datasets with multiple samples each, we investigated technical and biological factors that can contribute to this expression shift. Using two separate machine learning models we found that, in addition to expression level, RNA integrity, gene or UTR3 length, and the number of transcripts potentially also influence the occurrence of zeros. These findings could enable the development of novel analytical methods for cross-platform expression shift correction. We also identified genes and biological pathways in our diverse datasets that consistently showed differences when assessed at the single cell versus bulk level to assist in interpreting analysis across transcriptomic platforms. At the gene level, 25 genes (0.12%) were found in all datasets as discordant, but at the pathway level, 7 pathways (2.02%) showed shared enrichment in discordant genes.
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Affiliation(s)
- Joanna Zyla
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
| | - Anna Papiez
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
| | - Jun Zhao
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Rihao Qu
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Xiaotong Li
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Yuval Kluger
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
- Applied Mathematics Program, Yale University, New Haven, CT, USA
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
| | - Christos Hatzis
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Lajos Pusztai
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Michal Marczyk
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
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19
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Athaya T, Ripan RC, Li X, Hu H. Multimodal deep learning approaches for single-cell multi-omics data integration. Brief Bioinform 2023; 24:bbad313. [PMID: 37651607 PMCID: PMC10516349 DOI: 10.1093/bib/bbad313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/23/2023] [Accepted: 07/18/2023] [Indexed: 09/02/2023] Open
Abstract
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.
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Affiliation(s)
- Tasbiraha Athaya
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Rony Chowdhury Ripan
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, Florida, United States of America
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
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20
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Charitakis N, Salim A, Piers AT, Watt KI, Porrello ER, Elliott DA, Ramialison M. Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods. Genome Biol 2023; 24:209. [PMID: 37723583 PMCID: PMC10506280 DOI: 10.1186/s13059-023-03045-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 08/21/2023] [Indexed: 09/20/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
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Affiliation(s)
- Natalie Charitakis
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
| | - Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, Bouverie St, Carlton, VIC, 3053, Australia
- School of Mathematics and Statistics, University of Melbourne, Swanston Street, Parkville, VIC, 3010, Australia
| | - Adam T Piers
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, VIC, 3052, Australia
| | - Kevin I Watt
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Department of Diabetes, Monash University, Alfred Centre, Commercial Road, Melbourne, VIC, 3004, Australia
| | - Enzo R Porrello
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
| | - David A Elliott
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
| | - Mirana Ramialison
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
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21
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Huang LC, Stolze LK, Chen HC, Gelbard A, Shyr Y, Liu Q, Sheng Q. scDemultiplex: An iterative beta-binomial model-based method for accurate demultiplexing with hashtag oligos. Comput Struct Biotechnol J 2023; 21:4044-4055. [PMID: 37664174 PMCID: PMC10469060 DOI: 10.1016/j.csbj.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/15/2023] [Accepted: 08/15/2023] [Indexed: 09/05/2023] Open
Abstract
Single-cell sequencing have been widely used to characterize cellular heterogeneity. Sample multiplexing where multiple samples are pooled together for single-cell experiments, attracts wide attention due to its benefits of increasing capacity, reducing costs, and minimizing batch effects. To analyze multiplexed data, the first crucial step is to demultiplex, the process of assigning cells to individual samples. Inaccurate demultiplexing will create false cell types and result in misleading characterization. We propose scDemultiplex, which models hashtag oligo (HTO) counts with beta-binomial distribution and uses an iterative strategy for further refinement. Compared with seven existing demultiplexing approaches, scDemultiplex achieved great performance in both high-quality and low-quality data. Additionally, scDemultiplex can be combined with other approaches to improve their performance.
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Affiliation(s)
- Li-Ching Huang
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lindsey K. Stolze
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hua-Chang Chen
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Alexander Gelbard
- Department of Otolaryngology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Qi Liu
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Quanhu Sheng
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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22
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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23
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Abstract
The biology of a cell, whether it is a unicellular organism or part of a multicellular network, is influenced by cell type, temporal changes in cell state, and the cell's environment. Spatial cues play a critical role in the regulation of microbial pathogenesis strategies. Information about where the pathogen is-in a tissue or in proximity to a host cell-regulates gene expression and the compartmentalization of gene products in the microbe and the host. Our understanding of host and pathogen identity has bloomed with the accessibility of transcriptomics and proteomics techniques. A missing piece of the puzzle has been our ability to evaluate global transcript and protein expression in the context of the subcellular niche, primary cell, or native tissue environment during infection. This barrier is now lower with the advent of new spatial omics techniques to understand how location regulates cellular functions. This review will discuss how recent advances in spatial proteomics and transcriptomics approaches can address outstanding questions in microbial pathogenesis.
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Affiliation(s)
- Samantha Lempke
- Department of Microbiology, Immunology, and Cancer Biology at the Carter Immunology Center, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Dana May
- Department of Microbiology, Immunology, and Cancer Biology at the Carter Immunology Center, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Sarah E. Ewald
- Department of Microbiology, Immunology, and Cancer Biology at the Carter Immunology Center, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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24
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Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, Thakare RP, Banday S, Mishra AK, Das G, Malonia SK. Next-Generation Sequencing Technology: Current Trends and Advancements. BIOLOGY 2023; 12:997. [PMID: 37508427 PMCID: PMC10376292 DOI: 10.3390/biology12070997] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/09/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023]
Abstract
The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.
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Affiliation(s)
- Heena Satam
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Kandarp Joshi
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Upasana Mangrolia
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Sanober Waghoo
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Gulnaz Zaidi
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Shravani Rawool
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Ritesh P. Thakare
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
| | - Shahid Banday
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
| | - Alok K. Mishra
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
| | - Gautam Das
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Sunil K. Malonia
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
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25
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Cippà PE, McMahon AP. Proximal tubule responses to injury: interrogation by single-cell transcriptomics. Curr Opin Nephrol Hypertens 2023; 32:352-358. [PMID: 37074682 PMCID: PMC10330172 DOI: 10.1097/mnh.0000000000000893] [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] [Indexed: 04/20/2023]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) occurs in approximately 10-15% of patients admitted to hospital and is associated with adverse clinical outcomes. Despite recent advances, management of patients with AKI is still mainly supportive, including the avoidance of nephrotoxins, volume and haemodynamic management and renal replacement therapy. A better understanding of the renal response to injury is the prerequisite to overcome current limitations in AKI diagnostics and therapy. RECENT FINDINGS Single-cell technologies provided new opportunities to study the complexity of the kidney and have been instrumental for rapid advancements in the understanding of the cellular and molecular mechanisms of AKI. SUMMARY We provide an update on single-cell technologies and we summarize the recent discoveries on the cellular response to injury in proximal tubule cells from the early response in AKI, to the mechanisms of tubule repair and the relevance of maladaptive tubule repair in the transition to chronic kidney disease.
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Affiliation(s)
- Pietro E Cippà
- Division of Nephrology, Ente Ospedaliero Cantonale, Lugano, Switzerland
- Faculity of Biomedical Sciences, Università della Svizzera Italiana, Lugano Switzerland
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
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26
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Vöcking O, Famulski JK. Single cell transcriptome analyses of the developing zebrafish eye- perspectives and applications. Front Cell Dev Biol 2023; 11:1213382. [PMID: 37457291 PMCID: PMC10346855 DOI: 10.3389/fcell.2023.1213382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
Within a relatively short period of time, single cell transcriptome analyses (SCT) have become increasingly ubiquitous with transcriptomic research, uncovering plentiful details that boost our molecular understanding of various biological processes. Stemming from SCT analyses, the ever-growing number of newly assigned genetic markers increases our understanding of general function and development, while providing opportunities for identifying genes associated with disease. SCT analyses have been carried out using tissue from numerous organisms. However, despite the great potential of zebrafish as a model organism, other models are still preferably used. In this mini review, we focus on eye research as an example of the advantages in using zebrafish, particularly its usefulness for single cell transcriptome analyses of developmental processes. As studies have already shown, the unique opportunities offered by zebrafish, including similarities to the human eye, in combination with the possibility to analyze and extract specific cells at distinct developmental time points makes the model a uniquely powerful one. Particularly the practicality of collecting large numbers of embryos and therefore isolation of sufficient numbers of developing cells is a distinct advantage compared to other model organisms. Lastly, the advent of highly efficient genetic knockouts methods offers opportunities to characterize target gene function in a more cost-efficient way. In conclusion, we argue that the use of zebrafish for SCT approaches has great potential to further deepen our molecular understanding of not only eye development, but also many other organ systems.
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Affiliation(s)
| | - Jakub K. Famulski
- Department of Biology, University of Kentucky, Lexington, KY, United States
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27
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Koh JY, Affortit C, Ranum PT, West C, Walls WD, Yoshimura H, Shao JQ, Mostaert B, Smith RJH. Single-cell RNA-sequencing of stria vascularis cells in the adult Slc26a4 -/- mouse. BMC Med Genomics 2023; 16:133. [PMID: 37322474 PMCID: PMC10268361 DOI: 10.1186/s12920-023-01549-0] [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: 01/20/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND The primary pathological alterations of Pendred syndrome are endolymphatic pH acidification and luminal enlargement of the inner ear. However, the molecular contributions of specific cell types remain poorly characterized. Therefore, we aimed to identify pH regulators in pendrin-expressing cells that may contribute to the homeostasis of endolymph pH and define the cellular pathogenic mechanisms that contribute to the dysregulation of cochlear endolymph pH in Slc26a4-/- mice. METHODS We used single-cell RNA sequencing to identify both Slc26a4-expressing cells and Kcnj10-expressing cells in wild-type (WT, Slc26a4+/+) and Slc26a4-/- mice. Bioinformatic analysis of expression data confirmed marker genes defining the different cell types of the stria vascularis. In addition, specific findings were confirmed at the protein level by immunofluorescence. RESULTS We found that spindle cells, which express pendrin, contain extrinsic cellular components, a factor that enables cell-to-cell communication. In addition, the gene expression profile informed the pH of the spindle cells. Compared to WT, the transcriptional profiles in Slc26a4-/- mice showed downregulation of extracellular exosome-related genes in spindle cells. Immunofluorescence studies in spindle cells of Slc26a4-/- mice validated the increased expression of the exosome-related protein, annexin A1, and the clathrin-mediated endocytosis-related protein, adaptor protein 2. CONCLUSION Overall, cell isolation of stria vascularis from WT and Slc26a4-/- samples combined with cell type-specific transcriptomic analyses revealed pH-dependent alternations in spindle cells and intermediate cells, inspiring further studies into the dysfunctional role of stria vascularis cells in SLC26A4-related hearing loss.
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Affiliation(s)
- Jin-Young Koh
- Roy J. Carver Department of Biomedical Engineering, College of Engineering, University of Iowa, University of Iowa, Iowa City, IA, USA
- Molecular Otolaryngology and Renal Research Laboratories, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Corentin Affortit
- Molecular Otolaryngology and Renal Research Laboratories, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Paul T Ranum
- Molecular Otolaryngology and Renal Research Laboratories, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, The Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
| | - Cody West
- Molecular Otolaryngology and Renal Research Laboratories, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - William D Walls
- Molecular Otolaryngology and Renal Research Laboratories, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Hidekane Yoshimura
- Molecular Otolaryngology and Renal Research Laboratories, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Department of Otorhinolaryngology - Head and Neck Surgery, Shinshu University School of Medicine, Matsumoto, Japan
| | - Jian Q Shao
- Central Microscopy Research Facility, University of Iowa, Iowa City, IA, USA
| | - Brian Mostaert
- Department of Otolaryngology, Head and Neck Surgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Richard J H Smith
- Roy J. Carver Department of Biomedical Engineering, College of Engineering, University of Iowa, University of Iowa, Iowa City, IA, USA.
- Molecular Otolaryngology and Renal Research Laboratories, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
- Department of Otolaryngology, Head and Neck Surgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
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28
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Münch JM, Sobol MS, Brors B, Kaster AK. Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts. ADVANCES IN APPLIED MICROBIOLOGY 2023; 123:1-39. [PMID: 37400172 DOI: 10.1016/bs.aambs.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.
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Affiliation(s)
- Julia M Münch
- Institute for Biological Interfaces 5, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany; Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, Heidelberg, Germany; HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Morgan S Sobol
- Institute for Biological Interfaces 5, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany; HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Anne-Kristin Kaster
- Institute for Biological Interfaces 5, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany; HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany.
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29
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Delgado L, Navarrete M. Shining the Light on Astrocytic Ensembles. Cells 2023; 12:1253. [PMID: 37174653 PMCID: PMC10177371 DOI: 10.3390/cells12091253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
While neurons have traditionally been considered the primary players in information processing, the role of astrocytes in this mechanism has largely been overlooked due to experimental constraints. In this review, we propose that astrocytic ensembles are active working groups that contribute significantly to animal conduct and suggest that studying the maps of these ensembles in conjunction with neurons is crucial for a more comprehensive understanding of behavior. We also discuss available methods for studying astrocytes and argue that these ensembles, complementarily with neurons, code and integrate complex behaviors, potentially specializing in concrete functions.
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Affiliation(s)
| | - Marta Navarrete
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), 28002 Madrid, Spain
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30
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Pavel I, Irina L, Tatiana G, Denis P, Philipp K, Sergei K, Evgeny D. Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics. BMC Genomics 2023; 24:102. [PMID: 36882687 PMCID: PMC9990361 DOI: 10.1186/s12864-023-09192-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/17/2023] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics. RESULTS The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms. CONCLUSIONS GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.
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Affiliation(s)
- Iamshchikov Pavel
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia.,Laboratory of Translational Cellular and Molecular Biomedicine, Tomsk State University, Tomsk, 634050, Russia
| | - Larionova Irina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia. .,Laboratory of Translational Cellular and Molecular Biomedicine, Tomsk State University, Tomsk, 634050, Russia.
| | - Gerashchenko Tatiana
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia.,Laboratory of Single Cell Biology, Research Institute of Molecular and Cellular Medicine, Peoples' Friendship University of Russia (RUDN University), Moscow, 117198, Russia
| | - Piankov Denis
- Laboratory of Molecular Pathology, Genomed Ltd, Moscow, 115093, Russia
| | - Koshkin Philipp
- Laboratory of Molecular Pathology, Genomed Ltd, Moscow, 115093, Russia
| | - Korostelev Sergei
- Laboratory of Molecular Pathology, Genomed Ltd, Moscow, 115093, Russia
| | - Denisov Evgeny
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634009, Russia. .,Laboratory of Single Cell Biology, Research Institute of Molecular and Cellular Medicine, Peoples' Friendship University of Russia (RUDN University), Moscow, 117198, Russia.
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31
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Retinal Pigment Epithelium Cell Development: Extrapolating Basic Biology to Stem Cell Research. Biomedicines 2023; 11:biomedicines11020310. [PMID: 36830851 PMCID: PMC9952929 DOI: 10.3390/biomedicines11020310] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
The retinal pigment epithelium (RPE) forms an important cellular monolayer, which contributes to the normal physiology of the eye. Damage to the RPE leads to the development of degenerative diseases, such as age-related macular degeneration (AMD). Apart from acting as a physical barrier between the retina and choroidal blood vessels, the RPE is crucial in maintaining photoreceptor (PR) and visual functions. Current clinical intervention to treat early stages of AMD includes stem cell-derived RPE transplantation, which is still in its early stages of evolution. Therefore, it becomes essential to derive RPEs which are functional and exhibit features as observed in native human RPE cells. The conventional strategy is to use the knowledge obtained from developmental studies using various animal models and stem cell-based exploratory studies to understand RPE biogenies and developmental trajectory. This article emphasises such studies and aims to present a comprehensive understanding of the basic biology, including the genetics and molecular pathways of RPE development. It encompasses basic developmental biology and stem cell-based developmental studies to uncover RPE differentiation. Knowledge of the in utero developmental cues provides an inclusive methodology required for deriving RPEs using stem cells.
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32
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Mino-Kenudson M, Schalper K, Cooper W, Dacic S, Hirsch FR, Jain D, Lopez-Rios F, Tsao MS, Yatabe Y, Beasley MB, Yu H, Sholl LM, Brambilla E, Chou TY, Connolly C, Wistuba I, Kerr KM, Lantuejoul S. Predictive Biomarkers for Immunotherapy in Lung Cancer: Perspective From the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol 2022; 17:1335-1354. [PMID: 36184066 DOI: 10.1016/j.jtho.2022.09.109] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/08/2022] [Accepted: 09/12/2022] [Indexed: 10/14/2022]
Abstract
Immunotherapy including immune checkpoint inhibitors (ICIs) has become the backbone of treatment for most lung cancers with advanced or metastatic disease. In addition, they have increasingly been used for early stage tumors in neoadjuvant and adjuvant settings. Unfortunately, however, only a subset of patients experiences meaningful response to ICIs. Although programmed death-ligand 1 (PD-L1) protein expression by immunohistochemistry (IHC) has played a role as the principal predictive biomarker for immunotherapy, its performance may not be optimal, and it suffers multiple practical issues with different companion diagnostic assays approved. Similarly, tumor mutational burden (TMB) has multiple technical issues as a predictive biomarker for ICIs. Now, ongoing research on tumor- and host immune-specific factors has identified immunotherapy biomarkers that may provide better response and prognosis prediction, in particular in a multimodal approach. This review by the International Association for the Study of Lung Cancer Pathology Committee provides an overview of various immunotherapy biomarkers, including updated data on PD-L1 IHC and TMB, and assessments of neoantigens, genetic and epigenetic signatures, immune microenvironment by IHC and transcriptomics, and microbiome and pathologic response to neoadjuvant immunotherapies. The aim of this review is to underline the efficacy of new individual or combined predictive biomarkers beyond PD-L1 IHC and TMB.
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Affiliation(s)
- Mari Mino-Kenudson
- Department of Pathology, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts
| | - Kurt Schalper
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Wendy Cooper
- Royal Prince Alfred Hospital, NSW Health Pathology and University of Sydney, Camperdown, Australia
| | - Sanja Dacic
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Fred R Hirsch
- Center for Thoracic Oncology, The Tisch Cancer Institute, New York, New York; Icahn School of Medicine, Mount Sinai Health System, New York, New York
| | - Deepali Jain
- All India Institute of Medical Sciences, New Delhi, India
| | - Fernando Lopez-Rios
- Department of Pathology, "Doce de Octubre" University Hospital, Madrid, Spain
| | - Ming Sound Tsao
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | | | - Mary Beth Beasley
- Icahn School of Medicine, Mount Sinai Health System, New York, New York
| | - Hui Yu
- Center for Thoracic Oncology, The Tisch Cancer Institute, New York, New York; Icahn School of Medicine, Mount Sinai Health System, New York, New York
| | - Lynette M Sholl
- Department of Pathology, Brigham and Women's Hospital & Harvard Medical School, Boston, Massachusetts
| | | | | | - Casey Connolly
- International Association for the Study of Lung Cancer, Denver, Colorado
| | - Ignacio Wistuba
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Keith M Kerr
- Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Sylvie Lantuejoul
- Université Grenoble Alpes, Grenoble, France; Centre Léon Bérard Unicancer, Lyon, France.
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33
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Kujawa T, Marczyk M, Polanska J. Influence of single-cell RNA sequencing data integration on the performance of differential gene expression analysis. Front Genet 2022; 13:1009316. [PMID: 36386846 PMCID: PMC9663917 DOI: 10.3389/fgene.2022.1009316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/13/2022] [Indexed: 12/02/2022] Open
Abstract
Large-scale comprehensive single-cell experiments are often resource-intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data that might occur due to different technology platforms, reagent lots, or handling personnel. Such technical differences confound biological variations of interest and need to be corrected during the data integration process. Data integration is a challenging task due to the overlapping of biological and technical factors, which makes it difficult to distinguish their individual contribution to the overall observed effect. Moreover, the choice of integration method may impact the downstream analyses, including searching for differentially expressed genes. From the existing data integration methods, we selected only those that return the full expression matrix. We evaluated six methods in terms of their influence on the performance of differential gene expression analysis in two single-cell datasets with the same biological study design that differ only in the way the measurement was done: one dataset manifests strong batch effects due to the measurements of each sample at a different time. Integrated data were visualized using the UMAP method. The evaluation was done both on individual gene level using parametric and non-parametric approaches for finding differentially expressed genes and on gene set level using gene set enrichment analysis. As an evaluation metric, we used two correlation coefficients, Pearson and Spearman, of the obtained test statistics between reference, test, and corrected studies. Visual comparison of UMAP plots highlighted ComBat-seq, limma, and MNN, which reduced batch effects and preserved differences between biological conditions. Most of the tested methods changed the data distribution after integration, which negatively impacts the use of parametric methods for the analysis. Two algorithms, MNN and Scanorama, gave very poor results in terms of differential analysis on gene and gene set levels. Finally, we highlight ComBat-seq as it led to the highest correlation of test statistics between reference and corrected dataset among others. Moreover, it does not distort the original distribution of gene expression data, so it can be used in all types of downstream analyses.
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Affiliation(s)
- Tomasz Kujawa
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Michał Marczyk
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, United States
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
- *Correspondence: Joanna Polanska,
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34
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Geraldes I, Fernandes M, Fraga AG, Osório NS. The impact of single-cell genomics on the field of mycobacterial infection. Front Microbiol 2022; 13:989464. [PMID: 36246265 PMCID: PMC9562642 DOI: 10.3389/fmicb.2022.989464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.
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Affiliation(s)
- Inês Geraldes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Mónica Fernandes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Alexandra G. Fraga
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Nuno S. Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- *Correspondence: Nuno S. Osório
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De Wispelaere K, Freson K. The Analysis of the Human Megakaryocyte and Platelet Coding Transcriptome in Healthy and Diseased Subjects. Int J Mol Sci 2022; 23:ijms23147647. [PMID: 35886993 PMCID: PMC9317744 DOI: 10.3390/ijms23147647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Platelets are generated and released into the bloodstream from their precursor cells, megakaryocytes that reside in the bone marrow. Though platelets have no nucleus or DNA, they contain a full transcriptome that, during platelet formation, is transported from the megakaryocyte to the platelet. It has been described that transcripts in platelets can be translated into proteins that influence platelet response. The platelet transcriptome is highly dynamic and has been extensively studied using microarrays and, more recently, RNA sequencing (RNA-seq) in relation to diverse conditions (inflammation, obesity, cancer, pathogens and others). In this review, we focus on bulk and single-cell RNA-seq studies that have aimed to characterize the coding transcriptome of healthy megakaryocytes and platelets in humans. It has been noted that bulk RNA-seq has limitations when studying in vitro-generated megakaryocyte cultures that are highly heterogeneous, while single-cell RNA-seq has not yet been applied to platelets due to their very limited RNA content. Next, we illustrate how these methods can be applied in the field of inherited platelet disorders for gene discovery and for unraveling novel disease mechanisms using RNA from platelets and megakaryocytes and rare disease bioinformatics. Next, future perspectives are discussed on how this field of coding transcriptomics can be integrated with other next-generation technologies to decipher unexplained inherited platelet disorders in a multiomics approach.
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Szittner Z, Péter B, Kurunczi S, Székács I, Horváth R. Functional blood cell analysis by label-free biosensors and single-cell technologies. Adv Colloid Interface Sci 2022; 308:102727. [DOI: 10.1016/j.cis.2022.102727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 11/01/2022]
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Kent RS, Briggs EM, Colon BL, Alvarez C, Silva Pereira S, De Niz M. Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research. Front Cell Infect Microbiol 2022; 12:900878. [PMID: 35734575 PMCID: PMC9207352 DOI: 10.3389/fcimb.2022.900878] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.
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Affiliation(s)
- Robyn S. Kent
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, United States
| | - Emma M. Briggs
- Institute for Immunology and Infection Research, School of Biological Sciences, University Edinburgh, Edinburgh, United Kingdom
- Wellcome Centre for Integrative Parasitology, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Beatrice L. Colon
- Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - Catalina Alvarez
- de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - Sara Silva Pereira
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Mariana De Niz
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- Institut Pasteur, Paris, France
- *Correspondence: Mariana De Niz,
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Enabling automated and reproducible spatially resolved transcriptomics at scale. Heliyon 2022; 8:e09651. [PMID: 35756107 PMCID: PMC9213715 DOI: 10.1016/j.heliyon.2022.e09651] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/13/2022] [Accepted: 05/31/2022] [Indexed: 12/02/2022] Open
Abstract
Spatial information of tissues is an essential component to reach a holistic overview of gene expression mechanisms. The sequencing-based Spatial transcriptomics approach allows to spatially barcode the whole transcriptome of tissue sections using microarray glass slides. However, manual preparation of high-quality tissue sequencing libraries is time-consuming and subjected to technical variability. Here, we present an automated adaptation of the 10x Genomics Visium library construction on the widely used Agilent Bravo Liquid Handling Platform. Compared to the manual Visium library preparation, our automated approach reduces hands-on time by over 80% and provides higher throughput and robustness. Our automated Visium library preparation protocol provides a new strategy to standardize spatially resolved transcriptomics analysis of tissues at scale.
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Karagiannis TT, Monti S, Sebastiani P. Cell Type Diversity Statistic: An Entropy-Based Metric to Compare Overall Cell Type Composition Across Samples. Front Genet 2022; 13:855076. [PMID: 35464841 PMCID: PMC9023789 DOI: 10.3389/fgene.2022.855076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/18/2022] [Indexed: 12/31/2022] Open
Abstract
Changes of cell type composition across samples can carry biological significance and provide insight into disease and other conditions. Single cell transcriptomics has made it possible to study cell type composition at a fine resolution. Most single cell studies investigate compositional changes between samples for each cell type independently, not accounting for the fixed number of cells per sample in sequencing data. Here, we provide a metric of the distribution of cell type proportions in a sample that can be used to compare the overall distribution of cell types across multiple samples and biological conditions. This is the first method to measure overall cell type composition at the single cell level. We use the method to assess compositional changes in peripheral blood mononuclear cells (PBMCs) related to aging and extreme old age using multiple single cell datasets from individuals of four age groups across the human lifespan.
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Affiliation(s)
- Tanya T Karagiannis
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States
- Bioinformatics Program, Boston University, Boston, MA, United States
- *Correspondence: Tanya T Karagiannis,
| | - Stefano Monti
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Paola Sebastiani
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States
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Bothos E, Hatzis P, Moulos P. Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE. Methods Protoc 2022; 5:mps5020027. [PMID: 35314664 PMCID: PMC8938808 DOI: 10.3390/mps5020027] [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: 02/02/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022] Open
Abstract
The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research. The large data sets and the continually expanding applications for which they can be mined, such as the investigation of alternative splicing and others, have created novel challenges for data management, exploration, analysis, and visualization. Although a large variety of RNA-Seq data analysis software packages has emerged, both open-source and commercial, most fail to simultaneously address the above challenges, while they lack obvious functionalities, such as estimating RNA abundance over non-annotated genomic regions of interest in real time. We have developed SeqCVIBE, an R Shiny web application for the interactive exploration, analysis, visualization, and genome browsing of large RNA-Seq datasets. SeqCVIBE allows for multiple on-the-fly visualizations and calculations, such as differential expression analysis, averaging genomic signals over specific regions of the genome, and calculating RNA abundances over custom, potentially non-annotated regions, such as novel long non-coding RNAs. In addition, SeqCVIBE comprises a database for pre-analyzed data, where users can navigate and explore results, as well as perform a variety of basic on-the-fly analyses and export the outcomes. Finally, we demonstrate the value of SeqCVIBE in the elucidation of the interplay of a novel lincRNA, WiNTRLINC1, and Wnt signaling in colon cancer.
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Affiliation(s)
- Efthimios Bothos
- Institute of Communications and Computer Systems, National Technical University of Athens, 15780 Athens, Greece;
- HybridStat Predictive Analytics PC, Evrota 25, 14564 Kifisia, Greece
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center Alexander Fleming, Fleming 34, 16672 Vari, Greece;
| | - Panagiotis Moulos
- HybridStat Predictive Analytics PC, Evrota 25, 14564 Kifisia, Greece
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center Alexander Fleming, Fleming 34, 16672 Vari, Greece;
- Correspondence: ; Tel.: +30-210-9656310
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Martínez-Pérez A, Estévez O, González-Fernández Á. Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis. Front Microbiol 2022; 13:835620. [PMID: 35283833 PMCID: PMC8908424 DOI: 10.3389/fmicb.2022.835620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
While Tuberculosis (TB) infection remains a serious challenge worldwide, big data and “omic” approaches have greatly contributed to the understanding of the disease. Transcriptomics have been used to tackle a wide variety of queries including diagnosis, treatment evolution, latency and reactivation, novel target discovery, vaccine response or biomarkers of protection. Although a powerful tool, the elevated cost and difficulties in data interpretation may hinder transcriptomics complete potential. Technology evolution and collaborative efforts among multidisciplinary groups might be key in its exploitation. Here, we discuss the main fields explored in TB using transcriptomics, and identify the challenges that need to be addressed for a real implementation in TB diagnosis, prevention and therapy.
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Affiliation(s)
- Amparo Martínez-Pérez
- Biomedical Research Center (CINBIO), Universidade de Vigo, Vigo, Spain.,Hospital Álvaro Cunqueiro, Galicia Sur Health Research Institute (IIS-GS), Vigo, Spain
| | - Olivia Estévez
- Biomedical Research Center (CINBIO), Universidade de Vigo, Vigo, Spain.,Hospital Álvaro Cunqueiro, Galicia Sur Health Research Institute (IIS-GS), Vigo, Spain
| | - África González-Fernández
- Biomedical Research Center (CINBIO), Universidade de Vigo, Vigo, Spain.,Hospital Álvaro Cunqueiro, Galicia Sur Health Research Institute (IIS-GS), Vigo, Spain
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Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
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Li B, Hon GC. Single-Cell Genomics: Catalyst for Cell Fate Engineering. Front Bioeng Biotechnol 2021; 9:748942. [PMID: 34733831 PMCID: PMC8558416 DOI: 10.3389/fbioe.2021.748942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/05/2021] [Indexed: 12/14/2022] Open
Abstract
As we near a complete catalog of mammalian cell types, the capability to engineer specific cell types on demand would transform biomedical research and regenerative medicine. However, the current pace of discovering new cell types far outstrips our ability to engineer them. One attractive strategy for cellular engineering is direct reprogramming, where induction of specific transcription factor (TF) cocktails orchestrates cell state transitions. Here, we review the foundational studies of TF-mediated reprogramming in the context of a general framework for cell fate engineering, which consists of: discovering new reprogramming cocktails, assessing engineered cells, and revealing molecular mechanisms. Traditional bulk reprogramming methods established a strong foundation for TF-mediated reprogramming, but were limited by their small scale and difficulty resolving cellular heterogeneity. Recently, single-cell technologies have overcome these challenges to rapidly accelerate progress in cell fate engineering. In the next decade, we anticipate that these tools will enable unprecedented control of cell state.
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Affiliation(s)
- Boxun Li
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Gary C. Hon
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
- Division of Basic Reproductive Biology Research, Department of Obstetrics and Gynecology, Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, United States
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Dorado G, Gálvez S, Rosales TE, Vásquez VF, Hernández P. Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review. Biomolecules 2021; 11:1111. [PMID: 34439777 PMCID: PMC8393538 DOI: 10.3390/biom11081111] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/12/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023] Open
Abstract
Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.
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Affiliation(s)
- Gabriel Dorado
- Dep. Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, 14071 Córdoba, Spain
| | - Sergio Gálvez
- Dep. Lenguajes y Ciencias de la Computación, Boulevard Louis Pasteur 35, Universidad de Málaga, 29071 Málaga, Spain;
| | - Teresa E. Rosales
- Laboratorio de Arqueobiología, Avda. Universitaria s/n, Universidad Nacional de Trujillo, 13011 Trujillo, Peru;
| | - Víctor F. Vásquez
- Centro de Investigaciones Arqueobiológicas y Paleoecológicas Andinas Arqueobios, Martínez de Companón 430-Bajo 100, Urbanización San Andres, 13088 Trujillo, Peru;
| | - Pilar Hernández
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo s/n, 14080 Córdoba, Spain;
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