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McGovern MM, Hosamani IV, Niu Y, Nguyen KY, Zong C, Groves AK. Expression of Atoh1, Gfi1, and Pou4f3 in the mature cochlea reprograms nonsensory cells into hair cells. Proc Natl Acad Sci U S A 2024; 121:e2304680121. [PMID: 38266052 PMCID: PMC10835112 DOI: 10.1073/pnas.2304680121] [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: 03/22/2023] [Accepted: 12/08/2023] [Indexed: 01/26/2024] Open
Abstract
Mechanosensory hair cells of the mature mammalian organ of Corti do not regenerate; consequently, loss of hair cells leads to permanent hearing loss. Although nonmammalian vertebrates can regenerate hair cells from neighboring supporting cells, many humans with severe hearing loss lack both hair cells and supporting cells, with the organ of Corti being replaced by a flat epithelium of nonsensory cells. To determine whether the mature cochlea can produce hair cells in vivo, we reprogrammed nonsensory cells adjacent to the organ of Corti with three hair cell transcription factors: Gfi1, Atoh1, and Pou4f3. We generated numerous hair cell-like cells in nonsensory regions of the cochlea and new hair cells continued to be added over a period of 9 wk. Significantly, cells adjacent to reprogrammed hair cells expressed markers of supporting cells, suggesting that transcription factor reprogramming of nonsensory cochlear cells in adult animals can generate mosaics of sensory cells like those seen in the organ of Corti. Generating such sensory mosaics by reprogramming may represent a potential strategy for hearing restoration in humans.
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Affiliation(s)
- Melissa M McGovern
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - Ishwar V Hosamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030
| | - Yichi Niu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030
| | - Ken Y Nguyen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - Chenghang Zong
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030
| | - Andrew K Groves
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030
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Massimino M, Martorana F, Stella S, Vitale SR, Tomarchio C, Manzella L, Vigneri P. Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer. Genes (Basel) 2023; 14:1330. [PMID: 37510235 PMCID: PMC10380065 DOI: 10.3390/genes14071330] [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: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.
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Affiliation(s)
- Michele Massimino
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Silvia Rita Vitale
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Paolo Vigneri
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
- Humanitas Istituto Clinico Catanese, University Oncology Department, 95045 Catania, Italy
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Wang S, Sun ST, Zhang XY, Ding HR, Yuan Y, He JJ, Wang MS, Yang B, Li YB. The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives. Int J Mol Sci 2023; 24:ijms24032943. [PMID: 36769267 PMCID: PMC9918030 DOI: 10.3390/ijms24032943] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/01/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.
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Affiliation(s)
| | | | | | | | | | | | | | - Bin Yang
- Correspondence: (B.Y.); (Y.-B.L.)
| | - Yu-Bo Li
- Correspondence: (B.Y.); (Y.-B.L.)
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Adil A, Kumar V, Jan AT, Asger M. Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis. Front Neurosci 2021; 15:591122. [PMID: 33967674 PMCID: PMC8100238 DOI: 10.3389/fnins.2021.591122] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 03/19/2021] [Indexed: 11/17/2022] Open
Abstract
Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.
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Affiliation(s)
- Asif Adil
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Vijay Kumar
- Department of Biotechnology, Yeungnam University, Gyeongsan, South Korea
| | - Arif Tasleem Jan
- School of Biosciences and Biotechnology, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Mohammed Asger
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
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Wainer-Katsir K, Linial M. BIRD: identifying cell doublets via biallelic expression from single cells. Bioinformatics 2021; 36:i251-i257. [PMID: 32657402 PMCID: PMC7355245 DOI: 10.1093/bioinformatics/btaa474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Summary Current technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods raises the risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell experimental data focus on doublets of different species, cell types or individuals. In this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power toward doublets’ identification. We present a pipeline called BIallelic Ratio for Doublets (BIRD) that relies on heterologous genetic variations, from single-cell RNA sequencing. For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-seq data from 163 primary fibroblast single cells. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets were verified based on a biallelic expression signal amongst X-chromosome of female fibroblasts. Data from 10X Genomics microfluidics of human peripheral blood cells achieved in average 83% (±3.7%) accuracy, and an area under the curve of 0.88 (±0.04) for a collection of ∼13 300 single cells. BIRD addresses instances of doublets, which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved for high-coverage data from Smart-seq. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage and depth. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kerem Wainer-Katsir
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Givat Ram 91904, Israel
| | - Michal Linial
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Givat Ram 91904, Israel
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Regnault B, Bigot T, Ma L, Pérot P, Temmam S, Eloit M. Deep Impact of Random Amplification and Library Construction Methods on Viral Metagenomics Results. Viruses 2021; 13:v13020253. [PMID: 33562285 PMCID: PMC7915491 DOI: 10.3390/v13020253] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/27/2021] [Accepted: 02/03/2021] [Indexed: 12/16/2022] Open
Abstract
Clinical metagenomics is a broad-range agnostic detection method of pathogens, including novel microorganisms. A major limit is the low pathogen load compared to the high background of host nucleic acids. To overcome this issue, several solutions exist, such as applying a very high depth of sequencing, or performing a relative enrichment of viral genomes associated with capsids. At the end, the quantity of total nucleic acids is often below the concentrations recommended by the manufacturers of library kits, which necessitates to random amplify nucleic acids. Using a pool of 26 viruses representative of viral diversity, we observed a deep impact of the nature of sample (total nucleic acids versus RNA only), the reverse transcription, the random amplification and library construction method on virus recovery. We further optimized the two most promising methods and assessed their performance with fully characterized reference virus stocks. Good genome coverage and limit of detection lower than 100 or 1000 genome copies per mL of plasma, depending on the genome viral type, were obtained from a three million reads dataset. Our study reveals that optimized random amplification is a technique of choice when insufficient amounts of nucleic acid are available for direct libraries constructions.
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Affiliation(s)
- Béatrice Regnault
- Pathogen Discovery Laboratory, Institut Pasteur, 75015 Paris, France; (B.R.); (P.P.); (S.T.)
| | - Thomas Bigot
- Bioinformatics and Biostatistics Hub, Computational Biology Department, Institut Pasteur, 75015 Paris, France;
| | - Laurence Ma
- Biomics Platform, C2RT, Institut Pasteur, 75015 Paris, France;
| | - Philippe Pérot
- Pathogen Discovery Laboratory, Institut Pasteur, 75015 Paris, France; (B.R.); (P.P.); (S.T.)
| | - Sarah Temmam
- Pathogen Discovery Laboratory, Institut Pasteur, 75015 Paris, France; (B.R.); (P.P.); (S.T.)
| | - Marc Eloit
- Pathogen Discovery Laboratory, Institut Pasteur, 75015 Paris, France; (B.R.); (P.P.); (S.T.)
- Ecole Nationale Vétérinaire d’Alfort, 94700 Maisons-Alfort, France
- Correspondence: ; Tel.: +33-1-44-38-92-16
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