1
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Guo X, Fang Y, Liang R, Wang X, Zhang J, Dong C, Wang B, Liu Y, Chu M, Zhang X, Zhong R. Single-cell RNA-seq reveals the effects of the FecB mutation on the transcriptome profile in ovine cumulus cells. Sci Rep 2024; 14:13087. [PMID: 38849498 PMCID: PMC11161497 DOI: 10.1038/s41598-024-64001-y] [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: 11/12/2023] [Accepted: 06/04/2024] [Indexed: 06/09/2024] Open
Abstract
Genetic variations in the ovine ovulation rate, which are associated with the FecB mutation, provide useful models by which to explore the mechanisms regulating the development of mammalian antral follicles. In order to study the effects of the FecB mutation on cumulus cell differentiation, preovulatory follicles were aspirated and cumulus cells were isolated from three FecB genotypes (homozygous, heterozygous and wild type) of Small Tail Han (STH) sheep superstimulated with FSH. Transcriptome information from tens of thousands of cumulus cells was determined with the 10 × Genomics single-cell RNA-seq technology. Under the superovulation treatment, the observed number of preovulatory follicles in the ovaries of FecB carriers was still significantly higher than that in the wild-type (P < 0.05). The expression patterns of cumulus cells differed between FecB carriers and wild-type ewes. The screened cumulus cells could also be further divided into different cell clusters, and the differentiation states and fates of each group of cumulus cells also remained different, which supports the notion that heterogeneity in gene expression is prevalent in single cells. The oxidative phosphorylation pathway was significantly enriched in differentially expressed genes among the cell differentiation branch nodes of cumulus cells and among the differentially expressed genes of cumulus cells from the three genotypes. Combined with the important role of oxidative phosphorylation in the maturation of COCs, we suggest that the oxidative phosphorylation pathway of cumulus cells plays a crucial role in the differentiation process of cumulus cells and the mutation effect of the FecB gene.
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Affiliation(s)
- Xiaofei Guo
- Jilin Province Feed Processing and Ruminant Precision Breeding Cross Regional Cooperation Technology Innovation Center, Jilin Provincial Laboratory of Grassland Farming, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China
| | - Yi Fang
- Key Laboratory of Animal Production, Product Quality and Security, Ministry of Education, Jilin Agricultural University, Changchun, 130118, China
| | - Rong Liang
- Key Laboratory of Animal Production, Product Quality and Security, Ministry of Education, Jilin Agricultural University, Changchun, 130118, China
| | - Xiangyu Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Jinlong Zhang
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China
| | - Chunxiao Dong
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China
| | - Biao Wang
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China
| | - Yu Liu
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China
| | - Mingxing Chu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China.
| | - Xiaoshen Zhang
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China.
| | - Rongzhen Zhong
- Jilin Province Feed Processing and Ruminant Precision Breeding Cross Regional Cooperation Technology Innovation Center, Jilin Provincial Laboratory of Grassland Farming, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China.
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2
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Gupta P, O’Neill H, Wolvetang E, Chatterjee A, Gupta I. Advances in single-cell long-read sequencing technologies. NAR Genom Bioinform 2024; 6:lqae047. [PMID: 38774511 PMCID: PMC11106032 DOI: 10.1093/nargab/lqae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/18/2024] [Accepted: 04/29/2024] [Indexed: 05/24/2024] Open
Abstract
With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.
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Affiliation(s)
- Pallavi Gupta
- University of Queensland – IIT Delhi Research Academy, Hauz Khas, New Delhi 110016, India
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD 4072, Australia
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Hannah O’Neill
- Department of Pathology, Dunedin School of Medicine, University of Otago, 58 Hanover Street, Dunedin 9054, New Zealand
| | - Ernst J Wolvetang
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD 4072, Australia
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, 58 Hanover Street, Dunedin 9054, New Zealand
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
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3
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Wang Y, Luo Y, Guo X, Li Y, Yan J, Shao W, Wei W, Wei X, Yang T, Chen J, Chen L, Ding Q, Bai M, Zhuo L, Li L, Jackson D, Zhang Z, Xu X, Yan J, Liu H, Liu L, Yang N. A spatial transcriptome map of the developing maize ear. NATURE PLANTS 2024; 10:815-827. [PMID: 38745100 DOI: 10.1038/s41477-024-01683-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.
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Affiliation(s)
- Yuebin Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Yun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xing Guo
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- BGI Research, Wuhan, China
| | - Yunfu Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jiali Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Wenwen Shao
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- BGI Research, Wuhan, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Wenjie Wei
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xiaofeng Wei
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- China National GeneBank, Shenzhen, China
| | - Tao Yang
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- China National GeneBank, Shenzhen, China
| | - Jing Chen
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- China National GeneBank, Shenzhen, China
| | - Lihua Chen
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - Qian Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Minji Bai
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Lin Zhuo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Li Li
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - David Jackson
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Zuxin Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xun Xu
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Huan Liu
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China.
- Guangdong Laboratory of Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Shenzhen, China.
| | - Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
| | - Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
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4
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Kuwata T. Molecular classification and intratumoral heterogeneity of gastric adenocarcinoma. Pathol Int 2024. [PMID: 38651937 DOI: 10.1111/pin.13427] [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: 01/21/2024] [Revised: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
Gastric cancers frequently harbor striking histological complexity and diversity between lesions as well as within single lesions, known as inter- and intratumoral heterogeneity, respectively. The latest World Health Organization Classification of Tumors designated more than 30 histological subtypes for gastric epithelial tumors, assigning 12 subtypes for gastric adenocarcinoma (GAD). Meanwhile, recent advances in genome-wide analyses have provided molecular aspects to the histological classification of GAD, and consequently revealed different molecular traits underlying these histological subtypes. Moreover, accumulating knowledge of comprehensive molecular profiles has led to establishing molecular classifications of GAD, which are often associated with clinical biomarkers for therapeutics and prognosis. However, most of our knowledge of GAD molecular profiles is based on inter-tumoral heterogeneity, and the molecular profiles underlying intratumoral heterogeneity are yet to be determined. In this review, recently established molecular classifications of GAD are introduced in the aspect of pathological diagnosis and are discussed in the context of intratumoral heterogeneity.
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Affiliation(s)
- Takeshi Kuwata
- Department of Genetic Medicine and Services, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
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5
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Fan Y, Li L, Sun S. Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq. Genome Biol 2024; 25:96. [PMID: 38622747 PMCID: PMC11020788 DOI: 10.1186/s13059-024-03237-3] [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: 09/22/2023] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.
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Affiliation(s)
- Yue Fan
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Lei Li
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Shiquan Sun
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, 710061, People's Republic of China.
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6
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Godinez WJ, Trifonov V, Fang B, Kuzu G, Pei L, Guiguemde WA, Martin EJ, King FJ, Jenkins JL, Skewes-Cox P. Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning. J Chem Inf Model 2024; 64:2695-2704. [PMID: 38293736 DOI: 10.1021/acs.jcim.3c01855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce transcriptomics-to-activity transformer (TAT) models that leverage gene expression profiles observed over compound treatment at multiple concentrations to predict the compound activity in other biochemical or cellular assays. We built TAT models based on gene expression data from a RASL-seq assay to predict the activity of 2692 compounds in 262 dose-response assays. We obtained useful models for 51% of the assays, as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several submicromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.
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Affiliation(s)
- William J Godinez
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Vladimir Trifonov
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Bin Fang
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Guray Kuzu
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Luying Pei
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - W Armand Guiguemde
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Eric J Martin
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Frederick J King
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States
| | - Peter Skewes-Cox
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
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7
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Xu J, Chen H, Wang C, Ma Y, Song Y. Raman Flow Cytometry and Its Biomedical Applications. BIOSENSORS 2024; 14:171. [PMID: 38667164 PMCID: PMC11048678 DOI: 10.3390/bios14040171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024]
Abstract
Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.
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Affiliation(s)
- Jiayang Xu
- Zhejiang University-University of Edinburgh Institute, Zhejiang University, Hangzhou 310058, China;
- Edinburgh Medical School: Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Hongyi Chen
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, China
| | - Ce Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Yuting Ma
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Yizhi Song
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, China
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8
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Wang C, Chen L, Fu D, Liu W, Puri A, Kellis M, Yang J. Antigen presenting cells in cancer immunity and mediation of immune checkpoint blockade. Clin Exp Metastasis 2024:10.1007/s10585-023-10257-z. [PMID: 38261139 DOI: 10.1007/s10585-023-10257-z] [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: 06/02/2023] [Accepted: 12/06/2023] [Indexed: 01/24/2024]
Abstract
Antigen-presenting cells (APCs) are pivotal mediators of immune responses. Their role has increasingly been spotlighted in the realm of cancer immunology, particularly as our understanding of immunotherapy continues to evolve and improve. There is growing evidence that these cells play a non-trivial role in cancer immunity and have roles dependent on surface markers, growth factors, transcription factors, and their surrounding environment. The main dendritic cell (DC) subsets found in cancer are conventional DCs (cDC1 and cDC2), monocyte-derived DCs (moDC), plasmacytoid DCs (pDC), and mature and regulatory DCs (mregDC). The notable subsets of monocytes and macrophages include classical and non-classical monocytes, macrophages, which demonstrate a continuum from a pro-inflammatory (M1) phenotype to an anti-inflammatory (M2) phenotype, and tumor-associated macrophages (TAMs). Despite their classification in the same cell type, each subset may take on an immune-activating or immunosuppressive phenotype, shaped by factors in the tumor microenvironment (TME). In this review, we introduce the role of DCs, monocytes, and macrophages and recent studies investigating them in the cancer immunity context. Additionally, we review how certain characteristics such as abundance, surface markers, and indirect or direct signaling pathways of DCs and macrophages may influence tumor response to immune checkpoint blockade (ICB) therapy. We also highlight existing knowledge gaps regarding the precise contributions of different myeloid cell subsets in influencing the response to ICB therapy. These findings provide a summary of our current understanding of myeloid cells in mediating cancer immunity and ICB and offer insight into alternative or combination therapies that may enhance the success of ICB in cancers.
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Affiliation(s)
- Cassia Wang
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lee Chen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Doris Fu
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wendi Liu
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Anusha Puri
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jiekun Yang
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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9
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Ortiz-Perez A, Zhang M, Fitzpatrick LW, Izquierdo-Lozano C, Albertazzi L. Advanced optical imaging for the rational design of nanomedicines. Adv Drug Deliv Rev 2024; 204:115138. [PMID: 37980951 DOI: 10.1016/j.addr.2023.115138] [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: 06/09/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/21/2023]
Abstract
Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.
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Affiliation(s)
- Ana Ortiz-Perez
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Miao Zhang
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Laurence W Fitzpatrick
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Cristina Izquierdo-Lozano
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Lorenzo Albertazzi
- Department of Biomedical Engineering, Institute of Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands.
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10
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Pandit K, Surolia N, Bhattacharjee S, Karmodiya K. The many paths to artemisinin resistance in Plasmodium falciparum. Trends Parasitol 2023; 39:1060-1073. [PMID: 37833166 DOI: 10.1016/j.pt.2023.09.011] [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: 08/22/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
Emerging resistance against artemisinin (ART) poses a major challenge in controlling malaria. Parasites with mutations in PfKelch13, the major marker for ART resistance, are known to reduce hemoglobin endocytosis, induce unfolded protein response (UPR), elevate phosphatidylinositol-3-phosphate (PI3P) levels, and stimulate autophagy. Nonetheless, PfKelch13-independent resistance is also reported, indicating extensive complementation by reconfiguration in the parasite metabolome and transcriptome. These findings implicate that there may not be a single 'universal identifier' of ART resistance. This review sheds light on the molecular, transcriptional, and metabolic pathways associated with ART resistance, while also highlighting the interplay between cellular heterogeneity, environmental stress, and ART sensitivity.
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Affiliation(s)
- Kushankur Pandit
- Department of Biology, Indian Institute of Science Education and Research, Pune, India
| | - Namita Surolia
- Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India
| | - Souvik Bhattacharjee
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, India
| | - Krishanpal Karmodiya
- Department of Biology, Indian Institute of Science Education and Research, Pune, India.
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11
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Lin PY, Chang YT, Huang YC, Chen PY. Estimating genome-wide DNA methylation heterogeneity with methylation patterns. Epigenetics Chromatin 2023; 16:44. [PMID: 37941029 PMCID: PMC10634068 DOI: 10.1186/s13072-023-00521-7] [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: 03/27/2023] [Accepted: 10/30/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment. RESULTS Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection. CONCLUSIONS We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.
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Affiliation(s)
- Pei-Yu Lin
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
| | - Ya-Ting Chang
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
| | - Yu-Chun Huang
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, 115, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei, 115, Taiwan
| | - Pao-Yang Chen
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan.
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, 115, Taiwan.
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12
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Sheng X, Wu J, Wu X, Gong L, Su M, Tang J, Yang D, Wang W. Quantitative biochemical phenotypic heterogeneity of senescent macrophage at a single cell level by Synchrotron Radiation Fourier Transform Infrared Microspectroscopy. Mikrochim Acta 2023; 190:416. [PMID: 37768393 PMCID: PMC10539409 DOI: 10.1007/s00604-023-05980-z] [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: 04/05/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023]
Abstract
Macrophage senescence plays an important role in pathophysiological process of age-related diseases such as atherosclerosis, chronic obstructive pulmonary disease (COPD), pulmonary fibrosis, and lung cancer. After macrophage senescence, the biochemical phenotypes related to biological functions showed great heterogeneity. However, the biochemical phenotype and phenotypic heterogeneity of senescent macrophage has not been fully understood. Exploring the phenotype of biochemical substances in senescent macrophage will be helpful for understanding the function of senescent macrophage and finding out the potential mechanism between immune macrophage senescence and age-related diseases. In this study, we employed SR-FTIR microspectroscopy to detect the biochemical phenotype and phenotypic heterogeneity of single macrophage. The whole infrared spectra of senescent macrophages shifted, indicating biochemical substance changes within senescent macrophages. PCA and intercellular Euclidean distance statistical analysis based on specific spectra regions revealed dynamic changes of lipids and proteins during macrophage senescence. This proved that SR-FTIR microspectroscopy is an effective tool to detect the single cell biochemical phenotype transformation and phenotypic heterogeneity during macrophage senescence. It is of great significance to provide an evaluation method or clue for the study of cellular functions related to intracellular biochemical substances.
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Affiliation(s)
- Xiaolong Sheng
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China
| | - Jie Wu
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China
| | - Xun Wu
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China
| | - Lianghui Gong
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China
| | - Min Su
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China
| | - Jinming Tang
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China
| | - Desong Yang
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China.
| | - Wenxiang Wang
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
- Hunan Clinical Medical Research Center of Accurate Diagnosis and Treatment for Esophageal Carcinoma, Changsha, China.
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13
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Yaghoubi Naei V, Bordhan P, Mirakhorli F, Khorrami M, Shrestha J, Nazari H, Kulasinghe A, Ebrahimi Warkiani M. Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA. Ther Adv Med Oncol 2023; 15:17588359231192401. [PMID: 37692363 PMCID: PMC10486235 DOI: 10.1177/17588359231192401] [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: 11/24/2022] [Accepted: 07/19/2023] [Indexed: 09/12/2023] Open
Abstract
Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.
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Affiliation(s)
- Vahid Yaghoubi Naei
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pritam Bordhan
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Science, Institute for Biomedical Materials & Devices, University of Technology Sydney, Australia
| | - Fatemeh Mirakhorli
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Motahare Khorrami
- Immunology Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Jesus Shrestha
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Hojjatollah Nazari
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Arutha Kulasinghe
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Majid Ebrahimi Warkiani
- School of Biomedical Engineering, University of Technology Sydney, 1, Broadway, Ultimo New South Wales 2007, Australia
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14
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Sheng X, Xu J, Sun Y, Zhao J, Cao Y, Jiang L, Wu T, Lu H, Duan C, Hu J. Quantitative biochemical phenotypic heterogeneity of macrophages after myelin debris phagocytosis at a single cell level by synchrotron radiation fourier transform infrared microspectroscopy. Anal Chim Acta 2023; 1271:341434. [PMID: 37328242 DOI: 10.1016/j.aca.2023.341434] [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: 12/19/2022] [Revised: 05/21/2023] [Accepted: 05/26/2023] [Indexed: 06/18/2023]
Abstract
During the immuno-inflammatory pathophysiological process of spinal cord injury, traumatic brain injury, and ischemic stroke, macrophages play an important role in phagocytizing and clearing degenerated myelin debris. After phagocytizing myelin debris, the biochemical phenotypes related to the biological function of macrophages show vast heterogeneity; however, it is not fully understood. Detecting biochemical changes after myelin debris phagocytosis by macrophages at a single-cell level is helpful to characterize phenotypic and functional heterogeneity. In this study, based on the cell model of myelin debris phagocytosis by macrophages in vitro, the biochemical changes in macrophages were investigated using Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy. Infrared spectrum fluctuations, principal component analysis, and cell-to-cell Euclidean distance statistical analysis of specific spectrum regions revealed dynamic and significant changes in proteins and lipids within macrophages after myelin debris phagocytosis. Thus, SR-FTIR microspectroscopy is a powerful identification toolkit for exploring biochemical phenotype heterogeneity transformation that may be of great importance to providing an evaluation strategy for studying cell functions related to cellular substance distribution and metabolism.
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Affiliation(s)
- Xiaolong Sheng
- The Second Department of Thoracic Surgery, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, China; Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China
| | - Jiaqi Xu
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China
| | - Yi Sun
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China
| | - Jinyun Zhao
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China
| | - Yong Cao
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China
| | - Liyuan Jiang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China
| | - Tianding Wu
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China
| | - Hongbin Lu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China; Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Chunyue Duan
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China.
| | - Jianzhong Hu
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China.
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15
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Chen S, Zheng R, Tian L, Wu FX, Li M. A posterior probability based Bayesian method for single-cell RNA-seq data imputation. Methods 2023; 216:21-38. [PMID: 37315825 DOI: 10.1016/j.ymeth.2023.06.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/19/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) data suffer from a lot of zeros. Such dropout events impede the downstream data analyses. We propose BayesImpute to infer and impute dropouts from the scRNA-seq data. Using the expression rate and coefficient of variation of the genes within the cell subpopulation, BayesImpute first determines likely dropouts, and then constructs the posterior distribution for each gene and uses the posterior mean to impute dropout values. Some simulated and real experiments show that BayesImpute can effectively identify dropout events and reduce the introduction of false positive signals. Additionally, BayesImpute successfully recovers the true expression levels of missing values, restores the gene-to-gene and cell-to-cell correlation coefficient, and maintains the biological information in bulk RNA-seq data. Furthermore, BayesImpute boosts the clustering and visualization of cell subpopulations and improves the identification of differentially expressed genes. We further demonstrate that, in comparison to other statistical-based imputation methods, BayesImpute is scalable and fast with minimal memory usage.
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Affiliation(s)
- Siqi Chen
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Luyi Tian
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fang-Xiang Wu
- Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
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16
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Zhang X, Peng W, Fan J, Luo R, Liu S, Du W, Luo C, Zheng J, Pan X, Ge H. Regulatory role of Chitinase 3-like 1 gene in papillary thyroid carcinoma proved by integration analyses of single-cell sequencing with cohort and experimental validations. Cancer Cell Int 2023; 23:145. [PMID: 37480002 PMCID: PMC10362555 DOI: 10.1186/s12935-023-02987-7] [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: 03/28/2023] [Accepted: 07/06/2023] [Indexed: 07/23/2023] Open
Abstract
Papillary thyroid carcinoma (PTC) is one of the most common thyroid carcinomas. The gross extrathyroidal extension and extensive metastases of PTC lead to high rates of recurrence and poor clinical outcomes. However, the mechanisms underlying PTC development are poorly understood. In this study, using single-cell RNA sequencing, the transcriptome profiles of two PTC patients were addressed, including PTC1 with low malignancy and good prognosis and PTC2 with high malignancy and poor prognosis. We found that epithelial subcluster Epi02 was the most associated with the malignant development of PTC cells, with which the fold change of Chitinase 3-like 1 (CHI3L1) is on the top of the differentially expressed genes between PTC1 and PTC2 (P < 0.001). However CHI3L1 is rarely investigated in PTC as far. We then studied its role in PTC with a series of experiments. Firstly, qRT-PCR analysis of 14 PTC patients showed that the expression of CHI3L1 was positively correlated with malignancy. In addition, overexpression or silencing of CHI3L1 in TPC-1 cells, a PTC cell line, cultured in vitro showed that the proliferation, invasion, and metastasis of the cells were promoted or alleviated by CHI3L1. Further, immunohistochemistry analysis of 110 PTC cases revealed a significant relationship between CHI3L1 protein expression and PTC progression, especially the T (P < 0.001), N (P < 0.001), M stages (P = 0.007) and gross ETE (P < 0.001). Together, our results prove that CHI3L1 is a positive regulator of malignant development of PTC, and it promotes proliferation, invasion, and metastasis of PTC cells. Our study improves understanding of the molecular mechanisms underlying the progression of PTC and provides new insights for the clinical diagnosis and treatment of PTC.
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Affiliation(s)
- Xiaojun Zhang
- Department of Head Neck and Thyroid Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, China
| | - Wanwan Peng
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, 510515, Guangzhou, China
| | - Jie Fan
- Department of Head Neck and Thyroid Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, China
| | - Ruihua Luo
- Department of Head Neck and Thyroid Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, China
| | - Shanting Liu
- Department of Head Neck and Thyroid Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, China
| | - Wei Du
- Department of Head Neck and Thyroid Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, China
| | - Chaochao Luo
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, 510515, Guangzhou, China
| | - Jiawen Zheng
- Department of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, China
| | - Xinghua Pan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, 510515, Guangzhou, China.
- Department of Pediatrics, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.
- Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, 510515, Guangzhou, China.
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 450008, Zhengzhou, China.
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17
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Lee H, Lin PY, Chen PY. There's more to it: uncovering genomewide DNA methylation heterogeneity. Epigenomics 2023; 15:687-691. [PMID: 37485924 DOI: 10.2217/epi-2023-0228] [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: 07/25/2023] Open
Abstract
Tweetable abstract Monitoring changes in methylation heterogeneity can be powerful in detecting disease progression early. This editorial highlights the importance of profiling methylation heterogeneity and identifies existing measures and research gaps.
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Affiliation(s)
- HueyTyng Lee
- Institute of Plant & Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan
| | - Pei-Yu Lin
- Institute of Plant & Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan
| | - Pao-Yang Chen
- Institute of Plant & Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan
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18
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McDaniels JM, Shetty AC, Kuscu C, Kuscu C, Bardhi E, Rousselle T, Drachenberg C, Talwar M, Eason JD, Muthukumar T, Maluf DG, Mas VR. Single nuclei transcriptomics delineates complex immune and kidney cell interactions contributing to kidney allograft fibrosis. Kidney Int 2023; 103:1077-1092. [PMID: 36863444 PMCID: PMC10200746 DOI: 10.1016/j.kint.2023.02.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 01/16/2023] [Accepted: 02/07/2023] [Indexed: 03/04/2023]
Abstract
Chronic allograft dysfunction (CAD), characterized histologically by interstitial fibrosis and tubular atrophy, is the major cause of kidney allograft loss. Here, using single nuclei RNA sequencing and transcriptome analysis, we identified the origin, functional heterogeneity, and regulation of fibrosis-forming cells in kidney allografts with CAD. A robust technique was used to isolate individual nuclei from kidney allograft biopsies and successfully profiled 23,980 nuclei from five kidney transplant recipients with CAD and 17,913 nuclei from three patients with normal allograft function. Our analysis revealed two distinct states of fibrosis in CAD; low and high extracellular matrix (ECM) with distinct kidney cell subclusters, immune cell types, and transcriptional profiles. Imaging mass cytometry analysis confirmed increased ECM deposition at the protein level. Proximal tubular cells transitioned to an injured mixed tubular (MT1) phenotype comprised of activated fibroblasts and myofibroblast markers, generated provisional ECM which recruited inflammatory cells, and served as the main driver of fibrosis. MT1 cells in the high ECM state achieved replicative repair evidenced by dedifferentiation and nephrogenic transcriptional signatures. MT1 in the low ECM state showed decreased apoptosis, decreased cycling tubular cells, and severe metabolic dysfunction, limiting the potential for repair. Activated B, T and plasma cells were increased in the high ECM state, while macrophage subtypes were increased in the low ECM state. Intercellular communication between kidney parenchymal cells and donor-derived macrophages, detected several years post-transplantation, played a key role in injury propagation. Thus, our study identified novel molecular targets for interventions aimed to ameliorate or prevent allograft fibrogenesis in kidney transplant recipients.
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Affiliation(s)
- Jennifer M McDaniels
- Division of Surgical Sciences, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Amol C Shetty
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Cem Kuscu
- Transplant Research Institute, James D. Eason Transplant Institute, University of Tennessee Health Science Center, Memphis, Tennessee, USA; Department of Surgery, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Canan Kuscu
- Transplant Research Institute, James D. Eason Transplant Institute, University of Tennessee Health Science Center, Memphis, Tennessee, USA; Department of Surgery, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Elissa Bardhi
- Division of Surgical Sciences, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Thomas Rousselle
- Division of Surgical Sciences, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Cinthia Drachenberg
- Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Manish Talwar
- Transplant Research Institute, James D. Eason Transplant Institute, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - James D Eason
- Transplant Research Institute, James D. Eason Transplant Institute, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Thangamani Muthukumar
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Daniel G Maluf
- Division of Surgical Sciences, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA; Program in Transplantation, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Valeria R Mas
- Division of Surgical Sciences, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA.
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19
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Wiggins L, Lord A, Murphy KL, Lacy SE, O'Toole PJ, Brackenbury WJ, Wilson J. The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition. Nat Commun 2023; 14:1854. [PMID: 37012230 PMCID: PMC10070448 DOI: 10.1038/s41467-023-37447-3] [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: 10/19/2022] [Accepted: 03/14/2023] [Indexed: 04/05/2023] Open
Abstract
With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.
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Affiliation(s)
- Laura Wiggins
- York Biomedical Research Institute, University of York, York, UK
- Department of Biology, University of York, York, UK
| | - Alice Lord
- Department of Biology, University of York, York, UK
| | - Killian L Murphy
- Wolfson Atmospheric Chemistry Laboratories, University of York, York, UK
| | - Stuart E Lacy
- Wolfson Atmospheric Chemistry Laboratories, University of York, York, UK
| | - Peter J O'Toole
- York Biomedical Research Institute, University of York, York, UK
- Department of Biology, University of York, York, UK
| | - William J Brackenbury
- York Biomedical Research Institute, University of York, York, UK
- Department of Biology, University of York, York, UK
| | - Julie Wilson
- Department of Mathematics, University of York, York, UK.
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20
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Becker LM, Chen SH, Rodor J, de Rooij LPMH, Baker AH, Carmeliet P. Deciphering endothelial heterogeneity in health and disease at single-cell resolution: progress and perspectives. Cardiovasc Res 2023; 119:6-27. [PMID: 35179567 PMCID: PMC10022871 DOI: 10.1093/cvr/cvac018] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/16/2021] [Accepted: 02/16/2022] [Indexed: 11/14/2022] Open
Abstract
Endothelial cells (ECs) constitute the inner lining of vascular beds in mammals and are crucial for homeostatic regulation of blood vessel physiology, but also play a key role in pathogenesis of many diseases, thereby representing realistic therapeutic targets. However, it has become evident that ECs are heterogeneous, encompassing several subtypes with distinct functions, which makes EC targeting and modulation in diseases challenging. The rise of the new single-cell era has led to an emergence of studies aimed at interrogating transcriptome diversity along the vascular tree, and has revolutionized our understanding of EC heterogeneity from both a physiological and pathophysiological context. Here, we discuss recent landmark studies aimed at teasing apart the heterogeneous nature of ECs. We cover driving (epi)genetic, transcriptomic, and metabolic forces underlying EC heterogeneity in health and disease, as well as current strategies used to combat disease-enriched EC phenotypes, and propose strategies to transcend largely descriptive heterogeneity towards prioritization and functional validation of therapeutically targetable drivers of EC diversity. Lastly, we provide an overview of the most recent advances and hurdles in single EC OMICs.
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Affiliation(s)
| | | | | | | | - Andrew H Baker
- Corresponding authors. Tel: +32 16 32 62 47, E-mail: (P.C.); Tel: +44 (0)131 242 6774, E-mail: (A.H.B.)
| | - Peter Carmeliet
- Corresponding authors. Tel: +32 16 32 62 47, E-mail: (P.C.); Tel: +44 (0)131 242 6774, E-mail: (A.H.B.)
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21
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Silvano M, Virgolini N, Correia R, Clarke C, Isidro IA, Alves PM, Roldão A. Dissecting insect cell heterogeneity during influenza VLP production using single-cell transcriptomics. Front Bioeng Biotechnol 2023; 11:1143255. [PMID: 36949887 PMCID: PMC10025388 DOI: 10.3389/fbioe.2023.1143255] [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: 01/12/2023] [Accepted: 02/20/2023] [Indexed: 03/08/2023] Open
Abstract
The insect cell-baculovirus expression vector system (IC-BEVS) has been widely used to produce recombinant protein at high titers, including complex virus-like particles (VPLs). However, cell-to-cell variability upon infection is yet one of the least understood phenomena in virology, and little is known about its impact on production of therapeutic proteins. This study aimed at dissecting insect cell population heterogeneity during production of influenza VLPs in IC-BEVS using single-cell RNA-seq (scRNA-seq). High Five cell population was shown to be heterogeneous even before infection, with cell cycle being one of the factors contributing for this variation. In addition, infected insect cells were clustered according to the timing and level of baculovirus genes expression, with each cluster reporting similar influenza VLPs transgenes (i.e., hemagglutinin and M1) transcript counts. Trajectory analysis enabled to track infection progression throughout pseudotime. Specific pathways such as translation machinery, protein folding, sorting and degradation, endocytosis and energy metabolism were identified as being those which vary the most during insect cell infection and production of Influenza VLPs. Overall, this study lays the ground for the application of scRNA-seq in IC-BEVS processes to isolate relevant biological mechanisms during recombinant protein expression towards its further optimization.
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Affiliation(s)
- Marco Silvano
- iBET-Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Nikolaus Virgolini
- iBET-Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Ricardo Correia
- iBET-Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Colin Clarke
- NIBRT-National Institute for Bioprocessing Research and Training, Dublin, Ireland
- School of Chemical and Bioprocess Engineering, University College Dublin, Dublin, Ireland
| | - Inês A. Isidro
- iBET-Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Paula M. Alves
- iBET-Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - António Roldão
- iBET-Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
- *Correspondence: António Roldão,
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22
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Ho VTTX, Song MS, Kim G, Nguyen NB, Dao TP, Lee SY, Joo SW. Photothermal plasmonic microballs for non-contact single-cell calcium ionophore delivery in heterogeneous cells. Chem Commun (Camb) 2022; 59:195-198. [PMID: 36477026 DOI: 10.1039/d2cc05272e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Hierarchical plasmonic nanostructures comprising gold nanorod (AuNR)-covered microballs via syringe-injection reduction show good potential for selective single-cell calcium ionophore (A23187) delivery and apoptosis induction in heterogenous cancer cells.
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Affiliation(s)
- Vuong Thi Thanh Xuan Ho
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul, 06978, Republic of Korea.
| | - Min Seok Song
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul, 06978, Republic of Korea.
| | - Gun Kim
- Laboratory of Veterinary Pharmacology, College of Veterinary Medicine and Research Institute of Veterinary Science, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.
| | - Nguyen Binh Nguyen
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul, 06978, Republic of Korea.
| | - Thi Phuong Dao
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul, 06978, Republic of Korea.
| | - So Yeong Lee
- Laboratory of Veterinary Pharmacology, College of Veterinary Medicine and Research Institute of Veterinary Science, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.
| | - Sang-Woo Joo
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul, 06978, Republic of Korea.
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23
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Pang J, Maienschein-Cline M, Koh TJ. Monocyte/Macrophage Heterogeneity during Skin Wound Healing in Mice. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 209:1999-2011. [PMID: 36426946 PMCID: PMC9643652 DOI: 10.4049/jimmunol.2200365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 09/07/2022] [Indexed: 12/31/2022]
Abstract
Monocytes (Mos)/macrophages (Mϕs) orchestrate biological processes critical for efficient skin wound healing. However, current understanding of skin wound Mo/Mϕ heterogeneity is limited by traditional experimental approaches such as flow cytometry and immunohistochemistry. Therefore, we sought to more fully explore Mo/Mϕ heterogeneity and associated state transitions during the course of excisional skin wound healing in mice using single-cell RNA sequencing. The live CD45+CD11b+Ly6G- cells were isolated from skin wounds of C57BL/6 mice on days 3, 6, and 10 postinjury and captured using the 10x Genomics Chromium platform. A total of 2813 high-quality cells were embedded into a uniform manifold approximation and projection space, and eight clusters of distinctive cell populations were identified. Cluster dissimilarity and differentially expressed gene analysis categorized those clusters into three groups: early-stage/proinflammatory, late-stage/prohealing, and Ag-presenting phenotypes. Signature gene and Gene Ontology analysis of each cluster provided clues about the different functions of the Mo/Mϕ subsets, including inflammation, chemotaxis, biosynthesis, angiogenesis, proliferation, and cell death. Quantitative PCR assays validated characteristics of early- versus late-stage Mos/Mϕs inferred from our single-cell RNA sequencing dataset. Additionally, cell trajectory analysis by pseudotime and RNA velocity and adoptive transfer experiments indicated state transitions between early- and late-state Mos/Mϕs as healing progressed. Finally, we show that the chemokine Ccl7, which was a signature gene for early-stage Mos/Mϕs, preferentially induced the accumulation of proinflammatory Ly6C+F4/80lo/- Mos/Mϕs in mouse skin wounds. In summary, our data demonstrate the complexity of Mo/Mϕ phenotypes, their dynamic behavior, and diverse functions during normal skin wound healing.
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Affiliation(s)
- Jingbo Pang
- Center for Wound Healing and Tissue Regeneration, Department of Kinesiology and Nutrition, University of Illinois at Chicago, Chicago, IL 60612
| | | | - Timothy J. Koh
- Center for Wound Healing and Tissue Regeneration, Department of Kinesiology and Nutrition, University of Illinois at Chicago, Chicago, IL 60612
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24
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Hu K, Liu H, Lawson ND, Zhu LJ. scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data. Front Cell Dev Biol 2022; 10:981859. [PMID: 36238687 PMCID: PMC9551270 DOI: 10.3389/fcell.2022.981859] [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: 06/29/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.
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Affiliation(s)
- Kai Hu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Haibo Liu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Nathan D. Lawson
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Lihua Julie Zhu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Program in Molecular Medicine, Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
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25
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ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells. JOURNAL OF ONCOLOGY 2022; 2022:4106736. [PMID: 36090907 PMCID: PMC9452944 DOI: 10.1155/2022/4106736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/18/2022] [Accepted: 08/08/2022] [Indexed: 12/01/2022]
Abstract
The single-cell RNA sequencing (scRNA-seq) has recently been widely utilized to quantify transcriptomic profiles in single cells of bulk tumors. The transcriptomic profiles in single cells facilitate the investigation of intratumor heterogeneity that is unlikely confounded by the nontumor components. We proposed an algorithm (ATAXIC) to quantify the heterogeneity of transcriptomic perturbations (TPs) in single cancer cells. ATAXIC calculated the TP heterogeneity level of a single cell based on the standard deviations of the absolute z-scored gene expression values for tens of thousands of genes, reflecting the asynchronous degree of transcriptomic alterations relative to the central (mean) tendency. By analyzing scRNA-seq datasets for eight cancer types, we revealed that ATAXIC scores were likely to correlate positively with the enrichment scores of various proliferation and oncogenic signatures, DNA damage repair, treatment resistance, and unfavorable phenotypes and outcomes in cancer. The ATAXIC scores varied among different cancer types, with lung cancer and melanoma having the lowest average scores and clear cell renal cell carcinoma having the highest average scores. The low TP heterogeneity in lung cancer and melanoma could bestow relatively higher response rates to immune checkpoint inhibitors on both cancer types. In conclusion, ATAXIC is a useful algorithm to quantify the TP heterogeneity in single cancer cells, as well as providing new insights into tumor biology.
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26
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Khare K, Pandey R. Cellular heterogeneity in disease severity and clinical outcome: Granular understanding of immune response is key. Front Immunol 2022; 13:973070. [PMID: 36072602 PMCID: PMC9441806 DOI: 10.3389/fimmu.2022.973070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 07/26/2022] [Indexed: 12/15/2022] Open
Abstract
During an infectious disease progression, it is crucial to understand the cellular heterogeneity underlying the differential immune response landscape that will augment the precise information of the disease severity modulators, leading to differential clinical outcome. Patients with COVID-19 display a complex yet regulated immune profile with a heterogeneous array of clinical manifestation that delineates disease severity sub-phenotypes and worst clinical outcomes. Therefore, it is necessary to elucidate/understand/enumerate the role of cellular heterogeneity during COVID-19 disease to understand the underlying immunological mechanisms regulating the disease severity. This article aims to comprehend the current findings regarding dysregulation and impairment of immune response in COVID-19 disease severity sub-phenotypes and relate them to a wide array of heterogeneous populations of immune cells. On the basis of the findings, it suggests a possible functional correlation between cellular heterogeneity and the COVID-19 disease severity. It highlights the plausible modulators of age, gender, comorbidities, and hosts' genetics that may be considered relevant in regulating the host response and subsequently the COVID-19 disease severity. Finally, it aims to highlight challenges in COVID-19 disease that can be achieved by the application of single-cell genomics, which may aid in delineating the heterogeneity with more granular understanding. This will augment our future pandemic preparedness with possibility to identify the subset of patients with increased diseased severity.
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Affiliation(s)
- Kriti Khare
- Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Rajesh Pandey
- Immunology and Infectious Disease Biology, INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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27
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Wang Y, Dong C, Han Y, Gu Z, Sun C. Immunosenescence, aging and successful aging. Front Immunol 2022; 13:942796. [PMID: 35983061 PMCID: PMC9379926 DOI: 10.3389/fimmu.2022.942796] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Aging induces a series of immune related changes, which is called immunosenescence, playing important roles in many age-related diseases, especially neurodegenerative diseases, tumors, cardiovascular diseases, autoimmune diseases and coronavirus disease 2019(COVID-19). However, the mechanism of immunosenescence, the association with aging and successful aging, and the effects on diseases are not revealed obviously. In order to provide theoretical basis for preventing or controlling diseases effectively and achieve successful aging, we conducted the review and found that changes of aging-related phenotypes, deterioration of immune organ function and alterations of immune cell subsets participated in the process of immunosenescence, which had great effects on the occurrence and development of age-related diseases.
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Affiliation(s)
- Yunan Wang
- Department of Rheumatology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Chen Dong
- Department of Rheumatology, Affiliated Hospital of Nantong University, Nantong, China
| | - Yudian Han
- Information Center, The First People’s Hospital of Nantong City, Nantong, China
| | - Zhifeng Gu
- Department of Rheumatology, Affiliated Hospital of Nantong University, Nantong, China
- *Correspondence: Zhifeng Gu, ; Chi Sun,
| | - Chi Sun
- Department of Geriatrics, Affiliated Hospital of Nantong University, Nantong, China
- *Correspondence: Zhifeng Gu, ; Chi Sun,
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28
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Sharma M, Jha IP, Chawla S, Pandey N, Chandra O, Mishra S, Kumar V. Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs. Brief Bioinform 2022; 23:6623725. [PMID: 35772850 DOI: 10.1093/bib/bbac241] [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: 03/03/2022] [Revised: 05/22/2022] [Accepted: 05/23/2022] [Indexed: 11/14/2022] Open
Abstract
Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.
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Affiliation(s)
- Madhu Sharma
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Indra Prakash Jha
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Smriti Chawla
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Neetesh Pandey
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Omkar Chandra
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Shreya Mishra
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Vibhor Kumar
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
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29
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Mullen M, Wen Tan WL, Rhee JW, Wu JC. Modeling Susceptibility to Cardiotoxicity in Cancer Therapy Using Human iPSC-Derived Cardiac Cells and Systems Biology. Heart Fail Clin 2022; 18:335-347. [PMID: 35718410 DOI: 10.1016/j.hfc.2022.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The development of human-induced pluripotent stem cell-derived cardiac cell types has created a new paradigm in assessing drug-induced cardiotoxicity. Advances in genomics and epigenomics have also implicated several genomic loci and biological pathways that may contribute to susceptibility to cancer therapies. In this review, we first provide a brief overview of the cardiotoxicity associated with chemotherapy. We then provide a detailed summary of systems biology approaches being applied to elucidate potential molecular mechanisms involved in cardiotoxicity. Finally, we discuss combining systems biology approaches with iPSC technology to help discover molecular mechanisms associated with cardiotoxicity.
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Affiliation(s)
- McKay Mullen
- Stanford Cardiovascular Institute, Stanford University, 265 Campus Drive G1120B, Stanford, CA 94304, USA
| | - Wilson Lek Wen Tan
- Stanford Cardiovascular Institute, Stanford University, 265 Campus Drive G1120B, Stanford, CA 94304, USA
| | - June-Wha Rhee
- Department of Medicine, City of Hope Comprehensive Cancer Center, 1500 E Duarte Rd, Duarte, CA 91010, USA.
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University, 265 Campus Drive G1120B, Stanford, CA 94304, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University; Department of Radiology, Stanford University, 265 Campus Drive G1120B, Stanford, CA 94304, USA.
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30
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Mark C, Callander NS, Chng K, Miyamoto S, Warrick J. Timelapse viability assay to detect division and death of primary multiple myeloma cells in response to drug treatments with single cell resolution. Integr Biol (Camb) 2022; 14:49-61. [PMID: 35653717 PMCID: PMC9175638 DOI: 10.1093/intbio/zyac006] [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: 11/16/2021] [Revised: 03/15/2022] [Accepted: 04/12/2022] [Indexed: 11/12/2022]
Abstract
Heterogeneity among cancer cells and in the tumor microenvironment (TME) is thought to be a significant contributor to the heterogeneity of clinical therapy response observed between patients and can evolve over time. A primary example of this is multiple myeloma (MM), a generally incurable cancer where such heterogeneity contributes to the persistent evolution of drug resistance. However, there is a paucity of functional assays for studying this heterogeneity in patient samples or for assessing the influence of the patient TME on therapy response. Indeed, the population-averaged data provided by traditional drug response assays and the large number of cells required for screening remain significant hurdles to advancement. To address these hurdles, we developed a suite of accessible technologies for quantifying functional drug response to a panel of therapies in ex vivo three-dimensional culture using small quantities of a patient's own cancer and TME components. This suite includes tools for label-free single-cell identification and quantification of both cell division and death events with a standard brightfield microscope, an open-source software package for objective image analysis and feasible data management of multi-day timelapse experiments, and a new approach to fluorescent detection of cell death that is compatible with long-term imaging of primary cells. These new tools and capabilities are used to enable sensitive, objective, functional characterization of primary MM cell therapy response in the presence of TME components, laying the foundation for future studies and efforts to enable predictive assessment drug efficacy for individual patients.
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Affiliation(s)
- Christina Mark
- Cancer Biology Graduate Program, University of Wisconsin, Madison, WI 53705, USA
| | - Natalie S Callander
- University of Wisconsin Carbone Cancer Center, University of Wisconsin, Madison, WI 53705, USA
- Department of Medicine, University of Wisconsin, Madison, WI 53705, USA
| | - Kenny Chng
- McArdle Laboratory of Cancer Research, University of Wisconsin, Madison, WI 53705, USA
| | - Shigeki Miyamoto
- Cancer Biology Graduate Program, University of Wisconsin, Madison, WI 53705, USA
- University of Wisconsin Carbone Cancer Center, University of Wisconsin, Madison, WI 53705, USA
- McArdle Laboratory of Cancer Research, University of Wisconsin, Madison, WI 53705, USA
- Department of Oncology, University of Wisconsin, Madison, WI 53705, USA
| | - Jay Warrick
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53705, USA
- Salus Discovery, LLC, 110 E. Main St. Suite 815, Madison, WI 53703, USA
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31
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Dave B, Kanyal A, Mamatharani DV, Karmodiya K. Pervasive sequence-level variation in the transcriptome of Plasmodium falciparum. NAR Genom Bioinform 2022; 4:lqac036. [PMID: 35591889 PMCID: PMC9112769 DOI: 10.1093/nargab/lqac036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 03/09/2022] [Accepted: 05/14/2022] [Indexed: 12/05/2022] Open
Abstract
Single-nucleotide variations (SNVs) in RNA, arising from co- and post-transcriptional phenomena including transcription errors and RNA-editing, are well studied in a range of organisms. In the malaria parasite Plasmodium falciparum, stage-specific and non-specific gene-expression variations accompany the parasite's array of developmental and morphological phenotypes over the course of its complex life cycle. However, the extent, rate and effect of sequence-level variation in the parasite's transcriptome are unknown. Here, we report the presence of pervasive, non-specific SNVs in the P. falciparum transcriptome. SNV rates for a gene were correlated to gene length (r[Formula: see text]0.65-0.7) but not to the AT-content of that gene. Global SNV rates for the P. falciparum lines we used, and for publicly available P. vivax and P. falciparum clinical isolate datasets, were of the order of 10-3 per base, ∼10× higher than rates we calculated for bacterial datasets. These variations may reflect an intrinsic transcriptional error rate in the parasite, and RNA editing may be responsible for a subset of them. This seemingly characteristic property of the parasite may have implications for clinical outcomes and the basic biology and evolution of P. falciparum and parasite biology more broadly. We anticipate that our study will prompt further investigations into the exact sources, consequences and possible adaptive roles of these SNVs.
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Affiliation(s)
- Bruhad Dave
- Department of Biology, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, Maharashtra, India
| | - Abhishek Kanyal
- Department of Biology, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, Maharashtra, India
| | - D V Mamatharani
- Department of Biology, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, Maharashtra, India
| | - Krishanpal Karmodiya
- Department of Biology, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, Maharashtra, India
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32
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Tosevska A, Ghosh S, Ganguly A, Cappelletti M, Kallapur SG, Pellegrini M, Devaskar SU. Integrated analysis of an in vivo model of intra-nasal exposure to instilled air pollutants reveals cell-type specific responses in the placenta. Sci Rep 2022; 12:8438. [PMID: 35589747 PMCID: PMC9119931 DOI: 10.1038/s41598-022-12340-z] [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: 01/05/2022] [Accepted: 05/06/2022] [Indexed: 01/19/2023] Open
Abstract
The placenta is a heterogeneous organ whose development involves complex interactions of trophoblasts with decidual, vascular, and immune cells at the fetal-maternal interface. It maintains a critical balance between maternal and fetal homeostasis. Placental dysfunction can lead to adverse pregnancy outcomes including intra-uterine growth restriction, pre-eclampsia, or pre-term birth. Exposure to environmental pollutants contributes to the development of placental abnormalities, with poorly understood molecular underpinning. Here we used a mouse (C57BL/6) model of environmental pollutant exposure by administration of a particulate matter (SRM1649b at 300 μg/day/mouse) suspension intra-nasally beginning 2 months before conception and during gestation, in comparison to saline-exposed controls. Placental transcriptomes, at day 19 of gestation, were determined using bulk RNA-seq from whole placentas of exposed (n = 4) and control (n = 4) animals and scRNAseq of three distinct placental layers, followed by flow cytometry analysis of the placental immune cell landscape. Our results indicate a reduction in vascular placental cells, especially cells responsible for structural integrity, and increase in trophoblast proliferation in animals exposed to particulate matter. Pollution-induced inflammation was also evident, especially in the decidual layer. These data indicate that environmental exposure to air pollutants triggers changes in the placental cellular composition, mediating adverse pregnancy outcomes.
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Affiliation(s)
- Anela Tosevska
- grid.19006.3e0000 0000 9632 6718Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA USA ,grid.22937.3d0000 0000 9259 8492Present Address: Division of Rheumatology, Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Shubhamoy Ghosh
- grid.19006.3e0000 0000 9632 6718Division of Neonatology & Developmental Biology, Department of Pediatrics, and the UCLA Children’s Discovery & Innovation Institute, David Geffen School of Medicine at University of California Los Angeles, 10883, Le Conte Avenue, MDCC-22-412, Los Angeles, CA 90095-1752 USA
| | - Amit Ganguly
- grid.19006.3e0000 0000 9632 6718Division of Neonatology & Developmental Biology, Department of Pediatrics, and the UCLA Children’s Discovery & Innovation Institute, David Geffen School of Medicine at University of California Los Angeles, 10883, Le Conte Avenue, MDCC-22-412, Los Angeles, CA 90095-1752 USA
| | - Monica Cappelletti
- grid.19006.3e0000 0000 9632 6718Division of Neonatology & Developmental Biology, Department of Pediatrics, and the UCLA Children’s Discovery & Innovation Institute, David Geffen School of Medicine at University of California Los Angeles, 10883, Le Conte Avenue, MDCC-22-412, Los Angeles, CA 90095-1752 USA
| | - Suhas G. Kallapur
- grid.19006.3e0000 0000 9632 6718Division of Neonatology & Developmental Biology, Department of Pediatrics, and the UCLA Children’s Discovery & Innovation Institute, David Geffen School of Medicine at University of California Los Angeles, 10883, Le Conte Avenue, MDCC-22-412, Los Angeles, CA 90095-1752 USA
| | - Matteo Pellegrini
- grid.19006.3e0000 0000 9632 6718Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA USA
| | - Sherin U. Devaskar
- grid.19006.3e0000 0000 9632 6718Division of Neonatology & Developmental Biology, Department of Pediatrics, and the UCLA Children’s Discovery & Innovation Institute, David Geffen School of Medicine at University of California Los Angeles, 10883, Le Conte Avenue, MDCC-22-412, Los Angeles, CA 90095-1752 USA
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Qu R, He K, Yang Y, Fan T, Sun B, Khan AU, Huang W, Ouyang J, Pan X, Dai J. The role of serum amyloid A1 in the adipogenic differentiation of human adipose-derived stem cells basing on single-cell RNA sequencing analysis. Stem Cell Res Ther 2022; 13:187. [PMID: 35525990 PMCID: PMC9080218 DOI: 10.1186/s13287-022-02873-5] [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: 08/04/2021] [Accepted: 03/11/2022] [Indexed: 11/12/2022] Open
Abstract
Background Adipose-derived stem cells (ASCs) are obtained from a variety of sources in vivo where they present in large quantities. These cells are suitable for use in autologous transplantation and the construction of tissue-engineered adipose tissue. Studies have shown that ASCs differentiation is in a high degree of heterogeneity, yet the molecular basis including key regulators of differentiation remains to clarify. Methods We performed single-cell RNA sequencing and bioinformatics analysis on both undifferentiated (ASC-GM group) and adipogenically differentiated human ASCs (ASC-AD group, ASCs were cultured in adipogenic inducing medium for 1 week). And then, we verified the results of serum amyloid A1 (SAA1) with western blotting, immunofluorescence staining, oil red O staining. After these experiments, we down-regulated the expression of serum amyloid A1 (SAA1) gene to verify the adipogenic differentiation ability of ASCs.
Results In single-cell RNA sequence analyzing, we obtained 4415 cells in the ASC-GM group and 4634 cells in the ASC-AD group. The integrated sample cells could be divided into 11 subgroups (0–10 cluster). The cells in cluster 0, 2, 5 were came from ASC-GM group and the cells in cluster 1, 3, 7 came from ASC-AD group. The cells of cluster 4 and 6 came from both ASC-GM and ASC-AD groups. Fatty acid binding protein 4, fatty acid binding protein 5, complement factor D, fatty acid desaturase 1, and insulin like growth factor binding protein 5 were high expressed in category 1 and 7. Regulation of inflammatory response is the rank 1 biological processes. And cellular responses to external stimuli, negative regulation of defense response and acute inflammatory response are included in top 20 biological processes. Based on the MCODE results, we found that SAA1, C-C Motif Chemokine Ligand 5 (CCL5), and Annexin A1 (ANXA1) significantly highly expressed during adipogenic differentiation. Western blot and immunofluorescent staining results showed that SAA1 increased during adipogenesis. And the area of ORO positive staining in siSAA1 cells was significantly lower than in the siControl (negative control) cells. Conclusions Our results also indicated that our adipogenic induction was successful, and there was great heterogeneity in the adipogenic differentiation of ASCs. SAA1 with the regulation of inflammatory response were involved in adipogenesis of ASCs based on single-cell RNA sequencing analysis. The data obtained will help to elucidate the intrinsic mechanism of heterogeneity in the differentiation process of stem cells, thus, guiding the regulation of self-renewal and differentiation of adult stem cells. Supplementary Information The online version contains supplementary material available at 10.1186/s13287-022-02873-5.
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Affiliation(s)
- Rongmei Qu
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Kai He
- Guangdong Provincial Key Lab of Single Cell Technology and Application, and Department of Biochemistry and Molecular Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Yuchao Yang
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Tingyu Fan
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bing Sun
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Asmat Ullah Khan
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Jun Ouyang
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Xinghua Pan
- Guangdong Provincial Key Lab of Single Cell Technology and Application, and Department of Biochemistry and Molecular Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, China.
| | - Jingxing Dai
- Guangdong Provincial Key Laboratory of Medical Biomechanics and Guangdong Engineering Research Center for Translation of Medical 3D Printing Application and National Key Discipline of Human Anatomy, School of Basic Medical Science, Southern Medical University and National Demonstration Center for Experimental Education of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
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Lippolis JD, Putz EJ, Reinhardt TA, Casas E, Weber WJ, Crooker BA. Effect of Holstein genotype on immune response to an intramammary Escherichia coli challenge. J Dairy Sci 2022; 105:5435-5448. [DOI: 10.3168/jds.2021-21166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/08/2022] [Indexed: 11/19/2022]
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Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy. Cancers (Basel) 2022; 14:cancers14061384. [PMID: 35326534 PMCID: PMC8946040 DOI: 10.3390/cancers14061384] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/04/2022] [Accepted: 03/05/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Most malignant tumours are highly heterogeneous at molecular and phenotypic levels. Tumour variability poses challenges for the management of patients, as it arises between patients and even evolves in space and time within a single patient. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of tumour diversity in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate tumour heterogeneity. Abstract Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH.
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Park YL, Kim HP, Ock CY, Min DW, Kang JK, Lim YJ, Song SH, Han SW, Kim TY. EMT-mediated regulation of CXCL1/5 for resistance to anti-EGFR therapy in colorectal cancer. Oncogene 2022; 41:2026-2038. [PMID: 35173310 DOI: 10.1038/s41388-021-01920-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 05/08/2021] [Accepted: 06/18/2021] [Indexed: 02/07/2023]
Abstract
The emergence of RAS/RAF mutant clone is the main feature of EGFR inhibitor resistance in KRAS wild-type colon cancer. However, its molecular mechanism is thought to be multifactorial, mainly due to cellular heterogeneity. In order to better understand the resistance mechanism in a single clone level, we successfully isolated nine cells with cetuximab-resistant (CR) clonality from in vitro system. All CR cells harbored either KRAS or BRAF mutations. Characteristically, these cells showed a higher EMT (Epithelial to mesenchymal transition) signature, showing increased EMT markers such as SNAI2. Moreover, the expression level of CXCL1/5, a secreted protein, was significantly higher in CR cells compared to the parental cells. In these CR cells, CXCL1/5 expression was coordinately regulated by SNAI2/NFKB and transactivated EGFR through CXCR/MMPI/EGF axis via autocrine singling. We also observed that combined cetuximab/MEK inhibitor not only showed growth inhibition but also reduced the secreted amounts of CXCL1/5. We further found that serum CXCL1/5 level was positively correlated with the presence of RAS/RAF mutation in colon cancer patients during cetuximab therapy, suggesting its role as a biomarker. These data indicated that the application of serum CXCL1/5 could be a potential serologic biomarker for predicting resistance to EGFR therapy in colorectal cancer.
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Affiliation(s)
- Ye-Lim Park
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea. .,Cancer Research Institute, Seoul National University, Seoul, Korea.
| | - Hwang-Phill Kim
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.,Cancer Research Institute, Seoul National University, Seoul, Korea.,IMBDx Inc, Seoul, Korea
| | - Chan-Young Ock
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Dong-Wook Min
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Jun Kyu Kang
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.,Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Yoo Joo Lim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Sang-Hyun Song
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Sae-Won Han
- Cancer Research Institute, Seoul National University, Seoul, Korea.,Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Tae-You Kim
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea. .,Cancer Research Institute, Seoul National University, Seoul, Korea. .,Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
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Single-Cell Sequencing: Current Applications in Precision Onco-Genomics and Cancer Therapeutics. Cancers (Basel) 2022; 14:cancers14030657. [PMID: 35158925 PMCID: PMC8833749 DOI: 10.3390/cancers14030657] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/18/2022] [Accepted: 01/24/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Single-cell sequencing technologies are growing, advancing, and supporting new opportunities to better understand cancer. A variety of technologies are available that analyze the human transcriptome, genome, epigenome, and proteome, enabling integrated datasets. As a result, these integrated datasets contribute to new mechanistic insights and areas with therapeutic potential. This review summarizes the various single-cell sequencing techniques and provides examples of recent high-impact findings from the utilization of these technologies. Additionally, the translational relevance of these technologies and their use in clinical trials is described, along with the future potential for novel findings using these innovative methods. Abstract Single-cell sequencing encompasses a variety of technologies that evaluate cells at the genomic, transcriptomic, epigenomic, and proteomic levels. Each of these levels can be split into additional techniques that enable specific and optimized sequencing for a specialized purpose. At the transcriptomic level, single-cell sequencing has been used to understand immune-malignant cell networks, as well as differences between primary versus metastatic tumors. At the genomic and epigenomic levels, single-cell sequencing technology has been used to study genetic mutations involved in tumor evolution or the reprogramming of regulatory elements present in metastasized disease, respectively. Lastly, at the proteomic level, single-cell sequencing has been used to identify biomarkers important for predicting patient prognosis, as well as biomarkers essential for evaluating optimal treatment strategies. Integrated databases and atlases, as a result of large sequencing experiments, provide a vast array of information that can be applied to various studies and accessed by researchers to further answer scientific questions. This review summarizes recent, high-impact literature covering these aspects, as well as single-cell sequencing in the translational setting. Specifically, we review the potential that single-cell sequencing has in the clinic and its implementation in current clinical studies.
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Maurya R, Gohil N, Bhattacharjee G, Alzahrani KJ, Ramakrishna S, Singh V. Microfluidics for single cell analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 186:203-215. [PMID: 35033285 DOI: 10.1016/bs.pmbts.2021.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Cells have several internal molecules that are present in low amounts and any fluctuation in its number drives a change in cell behavior. These molecules present inside the cells are continuously fluctuating, thus producing noises in the intrinsic environment and thereby directly affecting the cellular behavior. Single-cell analysis using microfluidics is an important tool for monitoring cell behavior by analyzing internal molecules. Several gene circuits have been designed for this purpose that are labeled with fluorescence encoding genes for monitoring cell dynamics and behavior. We discuss herewith designed and fabricated microfluidics devices that are used for trapping and tracking cells under controlled environmental conditions. This chapter highlights microfluidics chip for monitoring cells to promote their basic understanding.
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Affiliation(s)
- Rupesh Maurya
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, Gujarat, India
| | - Nisarg Gohil
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, Gujarat, India
| | - Gargi Bhattacharjee
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, Gujarat, India
| | - Khalid J Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Suresh Ramakrishna
- Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, South Korea; College of Medicine, Hanyang University, Seoul, South Korea
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, Gujarat, India.
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Daga KR, Priyadarshani P, Larey AM, Rui K, Mortensen LJ, Marklein RA. Shape up before you ship out: morphology as a potential critical quality attribute for cellular therapies. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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40
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Wójtowicz K, Czogalla A, Trombik T, Łukaszewicz M. Surfactin cyclic lipopeptides change the plasma membrane composition and lateral organization in mammalian cells. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2021; 1863:183730. [PMID: 34419486 DOI: 10.1016/j.bbamem.2021.183730] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/10/2021] [Accepted: 08/13/2021] [Indexed: 02/07/2023]
Abstract
The specific structure and composition of the cell plasma membrane (PM) is crucial for many cellular processes and can be targeted by various substances with potential medical applications. In this context, biosurfactants (BS) constitute a promising group of natural compounds that possess several biological functions, including anticancer activity. Despite the efficiency of BS, their mode of action had never been elucidated before. Here, we demonstrate the influence of cyclic lipopeptide surfactin (SU) on the PM of CHO-K1 cells. Both FLIM and svFCS experiments show that even a low concentration of SU causes significant changes in the membrane fluidity and dynamic molecular organization. Further, we demonstrate that SU causes a relevant dose-dependent reduction of cellular cholesterol by extracting it from the PM. Finally, we show that CHO-25RA cells characterized by increased cholesterol levels are more sensitive to SU treatment than CHO-K1 cells. We propose that sterols organizing the PM raft nanodomains, constitute a potential target for SU and other biosurfactants. In our opinion, the anticancer activity of biosurfactants is directly related with the higher cholesterol content found in many cancer cells.
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Affiliation(s)
- Karolina Wójtowicz
- Department of Biotransformation, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Aleksander Czogalla
- Department of Cytobiochemistry, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Tomasz Trombik
- Department of Biophysics, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland.
| | - Marcin Łukaszewicz
- Department of Biotransformation, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland.
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Zeng X, Li X, Shao M, Xu Y, Shan W, Wei C, Li X, Wang L, Hu Y, Zhao Y, Qian P, Huang H. Integrated Single-Cell Bioinformatics Analysis Reveals Intrinsic and Extrinsic Biological Characteristics of Hematopoietic Stem Cell Aging. Front Genet 2021; 12:745786. [PMID: 34737765 PMCID: PMC8560737 DOI: 10.3389/fgene.2021.745786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/06/2021] [Indexed: 01/07/2023] Open
Abstract
Hematopoietic stem cell (HSC) aging, which is accompanied by loss of self-renewal capacity, myeloid-biased differentiation and increased risks of hematopoietic malignancies, is an important focus in stem cell research. However, the mechanisms underlying HSC aging have not been fully elucidated. In the present study, we integrated 3 independent single-cell transcriptome datasets of HSCs together and identified Stat3 and Ifngr1 as two markers of apoptosis-biased and inflammatory aged HSCs. Besides, common differentially expressed genes (DEGs) between young and aged HSCs were identified and further validated by quantitative RT-PCR. Functional enrichment analysis revealed that these DEGs were predominantly involved in the cell cycle and the tumor necrosis factor (TNF) signaling pathway. We further found that the Skp2-induced signaling pathway (Skp2→Cip1→CycA/CDK2→DP-1) contributed to a rapid transition through G1 phase in aged HSCs. In addition, analysis of the extrinsic alterations on HSC aging revealed the increased expression levels of inflammatory genes in bone marrow microenvironment. Colony formation unit assays showed that inflammatory cytokines promoted cellular senescence and that blockade of inflammatory pathway markedly rejuvenated aged HSC functions and increased B cell output. Collectively, our study elucidated the biological characteristics of HSC aging, and the genes and pathways we identified could be potential biomarkers and targets for the identification and rejuvenation of aged HSCs.
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Affiliation(s)
- Xiangjun Zeng
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Xia Li
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Mi Shao
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Yulin Xu
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Wei Shan
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Cong Wei
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Xiaoqing Li
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Limengmeng Wang
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Yongxian Hu
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Yanmin Zhao
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Pengxu Qian
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China.,Center of Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - He Huang
- Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China.,Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
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Rawat M, Srivastava A, Johri S, Gupta I, Karmodiya K. Single-Cell RNA Sequencing Reveals Cellular Heterogeneity and Stage Transition under Temperature Stress in Synchronized Plasmodium falciparum Cells. Microbiol Spectr 2021; 9:e0000821. [PMID: 34232098 PMCID: PMC8552519 DOI: 10.1128/spectrum.00008-21] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/26/2021] [Indexed: 12/12/2022] Open
Abstract
The malaria parasite has a complex life cycle exhibiting phenotypic and morphogenic variations in two different hosts by existing in heterogeneous developmental states. To investigate this cellular heterogeneity of the parasite within the human host, we performed single-cell RNA sequencing of synchronized Plasmodium cells under control and temperature treatment conditions. Using the Malaria Cell Atlas (https://www.sanger.ac.uk/science/tools/mca) as a guide, we identified 9 subtypes of the parasite distributed across known intraerythrocytic stages. Interestingly, temperature treatment results in the upregulation of the AP2-G gene, the master regulator of sexual development in a small subpopulation of the parasites. Moreover, we identified a heterogeneous stress-responsive subpopulation (clusters 5, 6, and 7 [∼10% of the total population]) that exhibits upregulation of stress response pathways under normal growth conditions. We also developed an online exploratory tool that will provide new insights into gene function under normal and temperature stress conditions. Thus, our study reveals important insights into cell-to-cell heterogeneity in the parasite population under temperature treatment that will be instrumental toward a mechanistic understanding of cellular adaptation and population dynamics in Plasmodium falciparum. IMPORTANCE The malaria parasite has a complex life cycle exhibiting phenotypic variations in two different hosts accompanied by cell-to-cell variability that is important for stress tolerance, immune evasion, and drug resistance. To investigate cellular heterogeneity determined by gene expression, we performed single-cell RNA sequencing (scRNA-seq) of about 12,000 synchronized Plasmodium cells under physiologically relevant normal (37°C) and temperature stress (40°C) conditions phenocopying the cyclic bouts of fever experienced during malarial infection. In this study, we found that parasites exhibit transcriptional heterogeneity in an otherwise morphologically synchronized culture. Also, a subset of parasites is continually committed to gametocytogenesis and stress-responsive pathways. These observations have important implications for understanding the mechanisms of drug resistance generation and vaccine development against the malaria parasite.
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Affiliation(s)
- Mukul Rawat
- Department of Biology, Indian Institute of Science Education and Research, Pashan, Pune, Maharashtra, India
| | - Ashish Srivastava
- Department of Biology, Indian Institute of Science Education and Research, Pashan, Pune, Maharashtra, India
| | - Shreya Johri
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Krishanpal Karmodiya
- Department of Biology, Indian Institute of Science Education and Research, Pashan, Pune, Maharashtra, India
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Qu R, He K, Fan T, Yang Y, Mai L, Lian Z, Zhou Z, Peng Y, Khan AU, Sun B, Huang X, Ouyang J, Pan X, Dai J, Huang W. Single-cell transcriptomic sequencing analyses of cell heterogeneity during osteogenesis of human adipose-derived mesenchymal stem cells. STEM CELLS (DAYTON, OHIO) 2021; 39:1478-1488. [PMID: 34346140 DOI: 10.1002/stem.3442] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 07/19/2021] [Indexed: 11/05/2022]
Abstract
Mesenchymal stem cells (MSCs) are known for their multilineage differentiation potential with immune-modulatory properties. The molecular underpinnings of differentiation remain largely undefined. In this study, we investigated the cellular and molecular features of chemically induced osteogenesis from MSC isolated from human adipose tissue (human adipose MSCs, hAMSCs) using single-cell RNA-sequencing (scRNA-seq). We found that a near complete differentiation of osteogenic clusters from hAMSCs under a directional induction. Both groups of cells are heterogeneous, and some of the hAMSCs cells are intrinsically prepared for osteogenesis, while variant OS clusters seems in cooperation with a due division of the general function. We identified a set of genes related to cell stress response highly expressed during the differentiation. We also characterized a series of transitional transcriptional waves throughout the process from hAMSCs to osteoblast and specified the unique gene networks and epigenetic status as key markers of osteogenesis.
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Affiliation(s)
- Rongmei Qu
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Kai He
- Guangdong Provincial Key Lab of Single Cell Technology and Application & Department of Biochemistry and Molecular Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, People's Republic of China
| | - Tingyu Fan
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Yuchao Yang
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Liyao Mai
- Guangdong Provincial Key Lab of Single Cell Technology and Application & Department of Biochemistry and Molecular Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, People's Republic of China
| | - Zhiwei Lian
- Guangdong Provincial Key Lab of Single Cell Technology and Application & Department of Biochemistry and Molecular Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, People's Republic of China
| | - Zhitao Zhou
- Central Laboratory, Southern Medical University, Guangzhou, People's Republic of China
| | - Yan Peng
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Asmat Ullah Khan
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Bing Sun
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Xiaolan Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Jun Ouyang
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Xinghua Pan
- Guangdong Provincial Key Lab of Single Cell Technology and Application & Department of Biochemistry and Molecular Biology, School of Basic Medical Science, Southern Medical University, Guangzhou, People's Republic of China
| | - Jingxing Dai
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics & Guangdong Engineering Research Center for Translation of Medical 3D Printing Application & National Demonstration Center for Experimental Education of Basic Medical Sciences & National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People's Republic of China
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Erdmann-Pham DD, Fischer J, Hong J, Song YS. A likelihood-based deconvolution of bulk gene expression data using single-cell references. Genome Res 2021; 31:1794-1806. [PMID: 34301624 DOI: 10.1101/gr.272344.120] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 07/02/2021] [Indexed: 11/24/2022]
Abstract
Direct comparison of bulk gene expression profiles is complicated by distinct cell type mixtures in each sample which obscure whether observed differences are actually due to changes in expression levels themselves or simply due to differing cell type compositions. Single-cell technology has made it possible to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects of differential expression and cell type mixtures. Here, we propose a generative model and a likelihood-based inference method that uses asymptotic statistical theory and a novel optimization procedure to perform deconvolution of bulk RNA-seq data to produce accurate cell type proportion estimates. We demonstrate the effectiveness of our method, called RNA-Sieve, across a diverse array of scenarios involving real data and discuss extensions made uniquely possible by our probabilistic framework, including a demonstration of well-calibrated confidence intervals.
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Chen X, Waller L, Chen J, Tang R, Zhang Z, Gagne I, Gutierrez B, Cho SH, Tseng CY, Lian IY, Lo YH. Label-free image-encoded microfluidic cell sorter with a scanning Bessel beam. APL PHOTONICS 2021; 6:076101. [PMID: 34263031 PMCID: PMC8259130 DOI: 10.1063/5.0051354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 06/23/2021] [Indexed: 05/03/2023]
Abstract
The microfluidic-based, label-free image-guided cell sorter offers a low-cost, high information content, and disposable solution that overcomes many limitations in conventional cell sorters. However, flow confinement for most microfluidic devices is generally only one-dimensional using sheath flow. As a result, the equilibrium distribution of cells spreads beyond the focal plane of commonly used Gaussian laser excitation beams, resulting in a large number of blurred images that hinder subsequent cell sorting based on cell image features. To address this issue, we present a Bessel-Gaussian beam image-guided cell sorter with an ultra-long depth of focus, enabling focused images of >85% of passing cells. This system features label-free sorting capabilities based on features extracted from the output temporal waveform of a photomultiplier tube (PMT) detector. For the sorting of polystyrene beads, SKNO1 leukemia cells, and Scenedesmus green algae, our results indicate a sorting purity of 97%, 97%, and 98%, respectively, showing that the temporal waveforms from the PMT outputs have strong correlations with cell image features. These correlations are also confirmed by off-line reconstructed cell images from a temporal-spatial transformation algorithm tailored to the scanning Bessel-Gaussian beam.
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Affiliation(s)
- Xinyu Chen
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California 92093, USA
| | - Lauren Waller
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, USA
| | - Jiajie Chen
- College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
- Authors to whom correspondence should be addressed: and
| | - Rui Tang
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California 92093, USA
| | - Zunming Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California 92093, USA
| | - Ivan Gagne
- NanoCellect Biomedical, Inc., San Diego, California 92121, USA
| | - Bien Gutierrez
- NanoCellect Biomedical, Inc., San Diego, California 92121, USA
| | - Sung Hwan Cho
- NanoCellect Biomedical, Inc., San Diego, California 92121, USA
| | - Chi-Yang Tseng
- Department of Materials Science and Engineering, University of California, San Diego, La Jolla, California 92093, USA
| | - Ian Y. Lian
- Department of Biology, Lamar University, Beaumont, Texas 77710, USA
| | - Yu-Hwa Lo
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California 92093, USA
- Authors to whom correspondence should be addressed: and
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Gregg RW, Shabnam F, Shoemaker JE. Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNβ production. Bioinformatics 2021; 37:1428-1434. [PMID: 33196784 DOI: 10.1093/bioinformatics/btaa969] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 10/13/2020] [Accepted: 11/04/2020] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNβ). Experimental evidence shows that IFNβ signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNβ production, an agent-based model is developed to simulate both DNA transfection and viral infection. RESULTS We show that heterogeneity can enhance IFNβ responses during infection. Furthermore, by varying the degree of IFNβ stochasticity, we find that only a percentage of cells (20-30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection. AVAILABILITY AND IMPLEMENTATION Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Robert W Gregg
- Department of Chemical and Petroleum Engineering, 15260, Pittsburgh, PA 15260, USA
| | - Fathima Shabnam
- Department of Chemical and Petroleum Engineering, 15260, Pittsburgh, PA 15260, USA
| | - Jason E Shoemaker
- Department of Chemical and Petroleum Engineering, 15260, Pittsburgh, PA 15260, USA.,McGowan Institute for Regenerative Medicine, 15219, Pittsburgh, PA 15260, USA.,Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
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Ji Y, Lotfollahi M, Wolf FA, Theis FJ. Machine learning for perturbational single-cell omics. Cell Syst 2021; 12:522-537. [PMID: 34139164 DOI: 10.1016/j.cels.2021.05.016] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/04/2021] [Accepted: 05/19/2021] [Indexed: 12/18/2022]
Abstract
Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
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Affiliation(s)
- Yuge Ji
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - F Alexander Wolf
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Cellarity, Cambridge, MA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany; Cellarity, Cambridge, MA, USA.
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Nguyen H, Tran D, Tran B, Pehlivan B, Nguyen T. A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data. Brief Bioinform 2021; 22:bbaa190. [PMID: 34020546 PMCID: PMC8138892 DOI: 10.1093/bib/bbaa190] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 06/19/2020] [Accepted: 07/24/2020] [Indexed: 12/13/2022] Open
Abstract
Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Duc Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bang Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bahadir Pehlivan
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
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Gala de Pablo J, Lindley M, Hiramatsu K, Goda K. High-Throughput Raman Flow Cytometry and Beyond. Acc Chem Res 2021; 54:2132-2143. [PMID: 33788539 DOI: 10.1021/acs.accounts.1c00001] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Flow cytometry is a powerful tool with applications in diverse fields such as microbiology, immunology, virology, cancer biology, stem cell biology, and metabolic engineering. It rapidly counts and characterizes large heterogeneous populations of cells in suspension (e.g., blood cells, stem cells, cancer cells, and microorganisms) and dissociated solid tissues (e.g., lymph nodes, spleen, and solid tumors) with typical throughputs of 1,000-100,000 events per second (eps). By measuring cell size, cell granularity, and the expression of cell surface and intracellular molecules, it provides systematic insights into biological processes. Flow cytometers may also include cell sorting capabilities to enable subsequent additional analysis of the sorted sample (e.g., electron microscopy and DNA/RNA sequencing), cloning, and directed evolution. Unfortunately, traditional flow cytometry has several critical limitations as it mainly relies on fluorescent labeling for cellular phenotyping, which is an indirect measure of intracellular molecules and surface antigens. Furthermore, it often requires time-consuming preparation protocols and is incompatible with cell therapy. To overcome these difficulties, a different type of flow cytometry based on direct measurements of intracellular molecules by Raman spectroscopy, or "Raman flow cytometry" for short, has emerged. Raman flow cytometry obtains a chemical fingerprint of the cell in a nondestructive manner, allowing for single-cell metabolic phenotyping. However, its slow signal acquisition due to the weak light-molecule interaction of spontaneous Raman scattering prevents the throughput necessary to interrogate large cell populations in reasonable time frames, resulting in throughputs of about 1 eps. The remedy to this throughput limit lies in coherent Raman scattering methods such as stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS), which offer a significantly enhanced light-sample interaction and hence enable high-throughput Raman flow cytometry, Raman imaging flow cytometry, and even Raman image-activated cell sorting (RIACS). In this Account, we outline recent advances, technical challenges, and emerging opportunities of coherent Raman flow cytometry. First, we review the principles of various types of SRS and CARS and introduce several techniques of coherent Raman flow cytometry such as CARS, multiplex CARS, Fourier-transform CARS, SRS, SRS imaging flow cytometry, and RIACS. Next, we discuss a unique set of applications enabled by coherent Raman flow cytometry, from microbiology and lipid biology to cancer detection and cell therapy. Finally, we describe future opportunities and challenges of coherent Raman flow cytometry including increasing sensitivity and throughput, integration with droplet microfluidics, utilizing machine learning techniques, or achieving in vivo flow cytometry. This Account summarizes the growing field of high-throughput Raman flow cytometry and the bright future it can bring.
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Affiliation(s)
- Julia Gala de Pablo
- Department of Chemistry, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Matthew Lindley
- Department of Chemistry, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Kotaro Hiramatsu
- Department of Chemistry, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Kanagawa Institute of Industrial Science and Technology, 705-1 Shimoimaizumi, Ebina, Kanagawa 243-0435, Japan
- Research Center for Spectrochemistry, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Bioengineering, University of California, 410 Westwood Plaza, Los Angeles, California 90095 United States
- Institute of Technological Sciences, Wuhan University, Wuchang District, Wuhan 430072, Hubei, China
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Taylor M, Lukowski JK, Anderton CR. Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:872-894. [PMID: 33656885 PMCID: PMC8033567 DOI: 10.1021/jasms.0c00439] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 05/02/2023]
Abstract
Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.
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Affiliation(s)
- Michael
J. Taylor
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jessica K. Lukowski
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Christopher R. Anderton
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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