151
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Kim J, Park J. Single-cell transcriptomics: a novel precision medicine technique in nephrology. Korean J Intern Med 2021; 36:479-490. [PMID: 33076636 PMCID: PMC8137400 DOI: 10.3904/kjim.2020.415] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/21/2020] [Indexed: 02/06/2023] Open
Abstract
Due to the complex structure and function of the kidneys, the mechanism of kidney disease is unclear. In particular, transcriptomics approaches at the bulk level are unable to differentiate primary autonomous responses, which lead to disease development, from secondary cell non-autonomous responses. Single-cell analysis techniques can overcome the limitations inherent in the measurement of heterogeneous cell populations and clarify the central issues in kidney biology and disease pathogenesis. Single-cell sequencing helps in identifying disease-related biomarkers and pathways, stratifying patients, and deciding on appropriate treatment methods. Here we review a variety of single-cell analysis techniques and single-cell transcriptomics studies performed in the field of nephrology. Moreover, we discuss the future prospects of single-cell analysis-based precision medicine in nephrology.
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Affiliation(s)
- Jisoo Kim
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Korea
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152
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Nagle MP, Tam GS, Maltz E, Hemminger Z, Wollman R. Bridging scales: From cell biology to physiology using in situ single-cell technologies. Cell Syst 2021; 12:388-400. [PMID: 34015260 DOI: 10.1016/j.cels.2021.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/30/2021] [Accepted: 03/08/2021] [Indexed: 12/14/2022]
Abstract
Biological organization crosses multiple spatial scales: from molecular, cellular, to tissues and organs. The proliferation of molecular profiling technologies enables increasingly detailed cataloging of the components at each scale. However, the scarcity of spatial profiling has made it challenging to bridge across these scales. Emerging technologies based on highly multiplexed in situ profiling are paving the way to study the spatial organization of cells and tissues in greater detail. These new technologies provide the data needed to cross the scale from cell biology to physiology and identify the fundamental principles that govern tissue organization. Here, we provide an overview of these key technologies and discuss the current and future insights these powerful techniques enable.
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Affiliation(s)
- Maeve P Nagle
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Gabriela S Tam
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Evan Maltz
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zachary Hemminger
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Roy Wollman
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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153
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Adil A, Kumar V, Jan AT, Asger M. Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis. Front Neurosci 2021; 15:591122. [PMID: 33967674 PMCID: PMC8100238 DOI: 10.3389/fnins.2021.591122] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 03/19/2021] [Indexed: 11/17/2022] Open
Abstract
Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.
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Affiliation(s)
- Asif Adil
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Vijay Kumar
- Department of Biotechnology, Yeungnam University, Gyeongsan, South Korea
| | - Arif Tasleem Jan
- School of Biosciences and Biotechnology, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Mohammed Asger
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
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154
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Bassiouni R, Gibbs LD, Craig DW, Carpten JD, McEachron TA. Applicability of spatial transcriptional profiling to cancer research. Mol Cell 2021; 81:1631-1639. [PMID: 33826920 PMCID: PMC8052283 DOI: 10.1016/j.molcel.2021.03.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/22/2021] [Accepted: 03/10/2021] [Indexed: 12/21/2022]
Abstract
Spatial transcriptional profiling provides gene expression information within the important anatomical context of tissue architecture. This approach is well suited to characterizing solid tumors, which develop within a complex landscape of malignant cells, immune cells, and stroma. In a single assay, spatial transcriptional profiling can interrogate the role of spatial relationships among these cell populations as well as reveal spatial patterns of relevant oncogenic genetic events. The broad utility of this approach is reflected in the array of strategies that have been developed for its implementation as well as in the recent commercial development of several profiling platforms. The flexibility to apply these technologies to both hypothesis-driven and discovery-driven studies allows widespread applicability in research settings. This review discusses available technologies for spatial transcriptional profiling and several applications for their use in cancer research.
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Affiliation(s)
- Rania Bassiouni
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - Lee D Gibbs
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - David W Craig
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - John D Carpten
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA
| | - Troy A McEachron
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, USA; Pediatric Oncology Branch, National Cancer Institute, 10 Center Drive, Bethesda, MD 20892, USA.
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155
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Gohil SH, Iorgulescu JB, Braun DA, Keskin DB, Livak KJ. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat Rev Clin Oncol 2021; 18:244-256. [PMID: 33277626 PMCID: PMC8415132 DOI: 10.1038/s41571-020-00449-x] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2020] [Indexed: 02/07/2023]
Abstract
Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution. This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour. Single-cell transcriptomics, the ability to track individual T cell clones through paired sequencing of the T cell receptor genes and high-dimensional single-cell spatial analysis are all areas of particular relevance to immuno-oncology. Multidimensional biomarker signatures will increasingly be crucial to guiding clinical decision-making in each patient with cancer. High-dimensional single-cell technologies are likely to provide the resolution and richness of data required to generate such clinically relevant signatures in immuno-oncology. In this Perspective, we describe advances made using transformative single-cell analysis technologies, especially in relation to clinical response and resistance to immunotherapy, and discuss the growing utility of single-cell approaches for answering important research questions.
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Affiliation(s)
- Satyen H Gohil
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Academic Haematology, University College London Cancer Institute, London, UK
| | - J Bryan Iorgulescu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David A Braun
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Derin B Keskin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kenneth J Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA.
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156
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Lareau CA, Ludwig LS, Muus C, Gohil SH, Zhao T, Chiang Z, Pelka K, Verboon JM, Luo W, Christian E, Rosebrock D, Getz G, Boland GM, Chen F, Buenrostro JD, Hacohen N, Wu CJ, Aryee MJ, Regev A, Sankaran VG. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat Biotechnol 2021; 39:451-461. [PMID: 32788668 PMCID: PMC7878580 DOI: 10.1038/s41587-020-0645-6] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 07/17/2020] [Indexed: 12/24/2022]
Abstract
Natural mitochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells. mtDNA can be profiled along with measures of cell state, but has not yet been combined with the massively parallel approaches needed to tackle the complexity of human tissue. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), a method that combines high-confidence mtDNA mutation calling in thousands of single cells with their concomitant high-quality accessible chromatin profile. This enables the inference of mtDNA heteroplasmy, clonal relationships, cell state and accessible chromatin variation in individual cells. We reveal single-cell variation in heteroplasmy of a pathologic mtDNA variant, which we associate with intra-individual chromatin variability and clonal evolution. We clonally trace thousands of cells from cancers, linking epigenomic variability to subclonal evolution, and infer cellular dynamics of differentiating hematopoietic cells in vitro and in vivo. Taken together, our approach enables the study of cellular population dynamics and clonal properties in vivo.
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Affiliation(s)
- Caleb A Lareau
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
| | - Leif S Ludwig
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Christoph Muus
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Satyen H Gohil
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Academic Haematology, UCL Cancer Institute, London, UK
| | - Tongtong Zhao
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Zachary Chiang
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Karin Pelka
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey M Verboon
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wendy Luo
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elena Christian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Daniel Rosebrock
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gad Getz
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Genevieve M Boland
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fei Chen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jason D Buenrostro
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Nir Hacohen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Catherine J Wu
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martin J Aryee
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
- Department of Biology and Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
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157
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Huang Q, Garrett A, Bose S, Blocker S, Rios AC, Clevers H, Shen X. The frontier of live tissue imaging across space and time. Cell Stem Cell 2021; 28:603-622. [PMID: 33798422 PMCID: PMC8034393 DOI: 10.1016/j.stem.2021.02.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
What you see is what you get-imaging techniques have long been essential for visualization and understanding of tissue development, homeostasis, and regeneration, which are driven by stem cell self-renewal and differentiation. Advances in molecular and tissue modeling techniques in the last decade are providing new imaging modalities to explore tissue heterogeneity and plasticity. Here we describe current state-of-the-art imaging modalities for tissue research at multiple scales, with a focus on explaining key tradeoffs such as spatial resolution, penetration depth, capture time/frequency, and moieties. We explore emerging tissue modeling and molecular tools that improve resolution, specificity, and throughput.
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Affiliation(s)
- Qiang Huang
- Department of Pediatric Surgery, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004 Shaanxi, China; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA
| | - Aliesha Garrett
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA
| | - Shree Bose
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA
| | - Stephanie Blocker
- Center for In Vitro Microscopy, Duke University, Durham, NC 27708, USA
| | - Anne C Rios
- Princess Máxima Center for Pediatric Oncology, Utrecht 3584, the Netherlands; Department of Cancer Research, Oncode Institute, Hubrecht Institute-KNAW Utrecht, Utrecht 3584, the Netherlands
| | - Hans Clevers
- Princess Máxima Center for Pediatric Oncology, Utrecht 3584, the Netherlands; Department of Cancer Research, Oncode Institute, Hubrecht Institute-KNAW Utrecht, Utrecht 3584, the Netherlands; Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and University Medical Center (UMC) Utrecht, Utrecht 3584, the Netherlands
| | - Xiling Shen
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA.
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158
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Ma J, Tran G, Wan AMD, Young EWK, Kumacheva E, Iscove NN, Zandstra PW. Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection. Sci Rep 2021; 11:6777. [PMID: 33762663 PMCID: PMC7990930 DOI: 10.1038/s41598-021-86087-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 03/10/2021] [Indexed: 01/31/2023] Open
Abstract
Gene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications.
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Affiliation(s)
- Jennifer Ma
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
| | - Gary Tran
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Alwin M D Wan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada
| | - Edmond W K Young
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada
| | - Eugenia Kumacheva
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada
- Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada
| | - Norman N Iscove
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada
| | - Peter W Zandstra
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada.
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
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159
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Mohr SE, Tattikota SG, Xu J, Zirin J, Hu Y, Perrimon N. Methods and tools for spatial mapping of single-cell RNAseq clusters in Drosophila. Genetics 2021; 217:6156631. [PMID: 33713129 DOI: 10.1093/genetics/iyab019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 02/02/2021] [Indexed: 01/26/2023] Open
Abstract
Single-cell RNA sequencing (scRNAseq) experiments provide a powerful means to identify clusters of cells that share common gene expression signatures. A major challenge in scRNAseq studies is to map the clusters to specific anatomical regions along the body and within tissues. Existing data, such as information obtained from large-scale in situ RNA hybridization studies, cell type specific transcriptomics, gene expression reporters, antibody stainings, and fluorescent tagged proteins, can help to map clusters to anatomy. However, in many cases, additional validation is needed to precisely map the spatial location of cells in clusters. Several approaches are available for spatial resolution in Drosophila, including mining of existing datasets, and use of existing or new tools for direct or indirect detection of RNA, or direct detection of proteins. Here, we review available resources and emerging technologies that will facilitate spatial mapping of scRNAseq clusters at high resolution in Drosophila. Importantly, we discuss the need, available approaches, and reagents for multiplexing gene expression detection in situ, as in most cases scRNAseq clusters are defined by the unique coexpression of sets of genes.
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Affiliation(s)
- Stephanie E Mohr
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Sudhir Gopal Tattikota
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Jun Xu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan Zirin
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Boston, MA 02115, USA
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160
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Payne AC, Chiang ZD, Reginato PL, Mangiameli SM, Murray EM, Yao CC, Markoulaki S, Earl AS, Labade AS, Jaenisch R, Church GM, Boyden ES, Buenrostro JD, Chen F. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 2021; 371:eaay3446. [PMID: 33384301 PMCID: PMC7962746 DOI: 10.1126/science.aay3446] [Citation(s) in RCA: 118] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 08/17/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022]
Abstract
Understanding genome organization requires integration of DNA sequence and three-dimensional spatial context; however, existing genome-wide methods lack either base pair sequence resolution or direct spatial localization. Here, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We applied IGS to human fibroblasts and early mouse embryos, spatially localizing thousands of genomic loci in individual nuclei. Using these data, we characterized parent-specific changes in genome structure across embryonic stages, revealed single-cell chromatin domains in zygotes, and uncovered epigenetic memory of global chromosome positioning within individual embryos. These results demonstrate how IGS can directly connect sequence and structure across length scales from single base pairs to whole organisms.
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Affiliation(s)
- Andrew C Payne
- Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | - Zachary D Chiang
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Paul L Reginato
- Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
- Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | | | - Evan M Murray
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | - Chun-Chen Yao
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | | | - Andrew S Earl
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Ajay S Labade
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Rudolf Jaenisch
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
- Department of Biology, MIT, Cambridge, MA 02139, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Edward S Boyden
- Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
- Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
- McGovern Institute, MIT, Cambridge, MA 02139, USA
- Koch Institute, MIT, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
- Centers for Neurobiological Engineering and Extreme Bionics, MIT, Cambridge, MA 02139, USA
| | - Jason D Buenrostro
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
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161
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Liu J, Xu T, Jin Y, Huang B, Zhang Y. Progress and Clinical Application of Single-Cell Transcriptional Sequencing Technology in Cancer Research. Front Oncol 2021; 10:593085. [PMID: 33614479 PMCID: PMC7886993 DOI: 10.3389/fonc.2020.593085] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 12/21/2020] [Indexed: 12/15/2022] Open
Abstract
Cancer has been a daunting challenge for human beings because of its clonal heterogeneity and compositional complexity. Tumors are composed of cancer cells and a variety of non-cancer cells, which together with the extracellular matrix form the tumor microenvironment. These cancer-related cells and components and immune mechanisms can affect the development and progression of cancer and are associated with patient diagnosis, treatment and prognosis. As the first choice for the study of complex biological systems, single-cell transcriptional sequencing (scRNA-seq) has been widely used in cancer research. ScRNA-seq has made breakthrough discoveries in tumor heterogeneity, tumor evolution, metastasis and spread, development of chemoresistance, and the relationship between the tumor microenvironment and the immune system. These results will guide clinical cancer treatment and promote personalized and highly accurate cancer treatment. In this paper, we summarize the latest research progress of scRNA-seq and its guiding significance for clinical treatment.
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Affiliation(s)
- Jian Liu
- Department of Gynaecology and Obstetrics, Jilin University Second Hospital, ChangChun, China
| | - Tianmin Xu
- Department of Gynaecology and Obstetrics, Jilin University Second Hospital, ChangChun, China
| | - Yuemei Jin
- Department of Gynaecology and Obstetrics, Jilin University Second Hospital, ChangChun, China
| | - Bingyu Huang
- Department of Gynaecology and Obstetrics, Jilin University Second Hospital, ChangChun, China
| | - Yan Zhang
- Department of Breast Surgery, Jilin University Second Hospital, ChangChun, China
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162
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Maniatis S, Petrescu J, Phatnani H. Spatially resolved transcriptomics and its applications in cancer. Curr Opin Genet Dev 2021; 66:70-77. [PMID: 33434721 PMCID: PMC7969406 DOI: 10.1016/j.gde.2020.12.002] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 02/06/2023]
Abstract
Spatially resolved transcriptomics (SRT) offers the promise of understanding cells and their modes of dysfunction in the context of intact tissues. Technologies for SRT have advanced rapidly with a large number being published in recent years. Diverse methods for SRT produce data at widely varying depth, throughput, accessibility and cost. Many published SRT methods have been demonstrated only in their labs of origin, while others have matured to the point of commercialization and widespread availability. Here we review technologies for SRT, and their application in studies of tumor heterogeneity.
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Affiliation(s)
- Silas Maniatis
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Joana Petrescu
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA; Department of Neurology, Division of Neuromuscular Medicine, Columbia University, New York, NY, USA
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA; Department of Neurology, Division of Neuromuscular Medicine, Columbia University, New York, NY, USA.
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163
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Somarakis A, Ijsselsteijn ME, Luk SJ, Kenkhuis B, de Miranda NFCC, Lelieveldt BPF, Hollt T. Visual cohort comparison for spatial single-cell omics-data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:733-743. [PMID: 33112747 DOI: 10.1109/tvcg.2020.3030336] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities.
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164
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Alon S, Goodwin DR, Sinha A, Wassie AT, Chen F, Daugharthy ER, Bando Y, Kajita A, Xue AG, Marrett K, Prior R, Cui Y, Payne AC, Yao CC, Suk HJ, Wang R, Yu CCJ, Tillberg P, Reginato P, Pak N, Liu S, Punthambaker S, Iyer EPR, Kohman RE, Miller JA, Lein ES, Lako A, Cullen N, Rodig S, Helvie K, Abravanel DL, Wagle N, Johnson BE, Klughammer J, Slyper M, Waldman J, Jané-Valbuena J, Rozenblatt-Rosen O, Regev A, Church GM, Marblestone AH, Boyden ES. Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. Science 2021; 371:eaax2656. [PMID: 33509999 PMCID: PMC7900882 DOI: 10.1126/science.aax2656] [Citation(s) in RCA: 188] [Impact Index Per Article: 62.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/13/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022]
Abstract
Methods for highly multiplexed RNA imaging are limited in spatial resolution and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to the mouse brain, which yielded the readout of thousands of genes, including splice variants. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in the neurons of the mouse hippocampus, revealing patterns across multiple cell types, layer-specific cell types across the mouse visual cortex, and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus, ExSeq enables highly multiplexed mapping of RNAs from nanoscale to system scale.
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Affiliation(s)
- Shahar Alon
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Faculty of Engineering, Gonda Brain Research Center and Institute of Nanotechnology, Bar-Ilan University, Ramat Gan, Israel
| | - Daniel R Goodwin
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Anubhav Sinha
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Asmamaw T Wassie
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Fei Chen
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Evan R Daugharthy
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Yosuke Bando
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Kioxia Corporation, Minato-ku, Tokyo, Japan
| | | | - Andrew G Xue
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | | | | | - Yi Cui
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Andrew C Payne
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chun-Chen Yao
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ho-Jun Suk
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Ru Wang
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Chih-Chieh Jay Yu
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Paul Tillberg
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | - Paul Reginato
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Nikita Pak
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA, USA
| | - Songlei Liu
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Sukanya Punthambaker
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Eswar P R Iyer
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Richie E Kohman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott Rodig
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Daniel L Abravanel
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Nikhil Wagle
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Michal Slyper
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Edward S Boyden
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA.
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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165
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de Jongh R, Spijkers XM, Pasteuning-Vuhman S, Vulto P, Pasterkamp RJ. Neuromuscular junction-on-a-chip: ALS disease modeling and read-out development in microfluidic devices. J Neurochem 2021; 157:393-412. [PMID: 33382092 DOI: 10.1111/jnc.15289] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 12/24/2020] [Accepted: 12/28/2020] [Indexed: 12/21/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal and progressive neurodegenerative disease affecting upper and lower motor neurons with no cure available. Clinical and animal studies reveal that the neuromuscular junction (NMJ), a synaptic connection between motor neurons and skeletal muscle fibers, is highly vulnerable in ALS and suggest that NMJ defects may occur at the early stages of the disease. However, mechanistic insight into how NMJ dysfunction relates to the onset and progression of ALS is incomplete, which hampers therapy development. This is, in part, caused by a lack of robust in vitro models. The ability to combine microfluidic and induced pluripotent stem cell (iPSC) technologies has opened up new avenues for studying molecular and cellular ALS phenotypes in vitro. Microfluidic devices offer several advantages over traditional culture approaches when modeling the NMJ, such as the spatial separation of different cell types and increased control over the cellular microenvironment. Moreover, they are compatible with 3D cell culture, which enhances NMJ functionality and maturity. Here, we review how microfluidic technology is currently being employed to develop more reliable in vitro NMJ models. To validate and phenotype such models, various morphological and functional read-outs have been developed. We describe and discuss the relevance of these read-outs and specifically illustrate how these read-outs have enhanced our understanding of NMJ pathology in ALS. Finally, we share our view on potential future directions and challenges.
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Affiliation(s)
- Rianne de Jongh
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Xandor M Spijkers
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands.,Mimetas B.V., Organ-on-a-chip Company, Leiden, The Netherlands
| | - Svetlana Pasteuning-Vuhman
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Paul Vulto
- Mimetas B.V., Organ-on-a-chip Company, Leiden, The Netherlands
| | - R Jeroen Pasterkamp
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
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166
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Kvastad L, Carlberg K, Larsson L, Villacampa EG, Stuckey A, Stenbeck L, Mollbrink A, Zamboni M, Magnusson JP, Basmaci E, Shamikh A, Prochazka G, Schaupp AL, Borg Å, Fugger L, Nistér M, Lundeberg J. The spatial RNA integrity number assay for in situ evaluation of transcriptome quality. Commun Biol 2021; 4:57. [PMID: 33420318 PMCID: PMC7794352 DOI: 10.1038/s42003-020-01573-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 11/11/2020] [Indexed: 12/03/2022] Open
Abstract
The RNA integrity number (RIN) is a frequently used quality metric to assess the completeness of rRNA, as a proxy for the corresponding mRNA in a tissue. Current methods operate at bulk resolution and provide a single average estimate for the whole sample. Spatial transcriptomics technologies have emerged and shown their value by placing gene expression into a tissue context, resulting in transcriptional information from all tissue regions. Thus, the ability to estimate RNA quality in situ has become of utmost importance to overcome the limitation with a bulk rRNA measurement. Here we show a new tool, the spatial RNA integrity number (sRIN) assay, to assess the rRNA completeness in a tissue wide manner at cellular resolution. We demonstrate the use of sRIN to identify spatial variation in tissue quality prior to more comprehensive spatial transcriptomics workflows.
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Affiliation(s)
- Linda Kvastad
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Konstantin Carlberg
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Eva Gracia Villacampa
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Alexander Stuckey
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Linnea Stenbeck
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Annelie Mollbrink
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden
| | - Margherita Zamboni
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jens Peter Magnusson
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
- Bioengineering Department, Stanford University, Stanford, USA
| | - Elisa Basmaci
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Alia Shamikh
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Gabriela Prochazka
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Anna-Lena Schaupp
- Nuffield Department of Clinical Neurosciences, MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital University of Oxford, Oxford Centre for Neuroinflammation, Oxford, UK
| | - Åke Borg
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund Lund University, Lund, Sweden
| | - Lars Fugger
- Nuffield Department of Clinical Neurosciences, MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital University of Oxford, Oxford Centre for Neuroinflammation, Oxford, UK
| | - Monica Nistér
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, KTH - Royal Institute of Technology (KTH), SE-171 65, Solna, Sweden.
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167
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Somarakis A, Van Unen V, Koning F, Lelieveldt B, Hollt T. ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:98-110. [PMID: 31369380 DOI: 10.1109/tvcg.2019.2931299] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.
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168
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Abrahams L. Single Cell Systems Analysis: Decision Geometry In Outliers. Bioinformatics 2020; 37:1747-1755. [PMID: 33367486 DOI: 10.1093/bioinformatics/btaa1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 11/28/2020] [Accepted: 12/16/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Anti-cancer therapeutics of the highest calibre currently focus on combinatorial targeting of specific oncoproteins and tumour suppressors. Clinical relapse depends upon intratumoral heterogeneity which serves as substrate variation during evolution of resistance to therapeutic regimens. RESULTS The present review advocates single cell systems biology as the optimal level of analysis for remediation of clinical relapse. Graph theory approaches to understanding decision-making in single cells may be abstracted one level further, to the geometry of decision-making in outlier cells, in order to define evolution-resistant cancer biomarkers. Systems biologists currently working with omics data are invited to consider phase portrait analysis as a mediator between graph theory and deep learning approaches. Perhaps counter-intuitively, the tangible clinical needs of cancer patients may depend upon the adoption of higher level mathematical abstractions of cancer biology. SUPPLEMENTARY INFORMATION supplementary data available at Bioinformatics online.
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Affiliation(s)
- Lianne Abrahams
- Ronin Institute, 127 Haddon Place, Montclair, New Jersey, 07043-2314, United States
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169
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A machine learning toolkit for genetic engineering attribution to facilitate biosecurity. Nat Commun 2020; 11:6293. [PMID: 33293535 PMCID: PMC7722865 DOI: 10.1038/s41467-020-19612-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 10/05/2020] [Indexed: 12/21/2022] Open
Abstract
The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed 'genetic engineering attribution', would deter misuse, yet is still considered unsolved. Here, we show that recurrent neural networks trained on DNA motifs and basic phenotype data can reach 70% attribution accuracy in distinguishing between over 1,300 labs. To make these models usable in practice, we introduce a framework for weighing predictions against other investigative evidence using calibration, and bring our model to within 1.6% of perfect calibration. Additionally, we demonstrate that simple models can accurately predict both the nation-state-of-origin and ancestor labs, forming the foundation of an integrated attribution toolkit which should promote responsible innovation and international security alike.
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170
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Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther 2020; 221:107753. [PMID: 33259885 DOI: 10.1016/j.pharmthera.2020.107753] [Citation(s) in RCA: 691] [Impact Index Per Article: 172.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 11/23/2020] [Indexed: 12/23/2022]
Abstract
Tumor microenvironment denotes the non-cancerous cells and components presented in the tumor, including molecules produced and released by them. The constant interactions between tumor cells and the tumor microenvironment play decisive roles in tumor initiation, progression, metastasis, and response to therapies. The tumor microenvironment as a therapeutic target in cancer has attracted great research and clinical interest. Here we summarize the current progress in targeting the tumor microenvironment in both drug development and clinical trials; highlight challenges in targeting the tumor microenvironment to achieve therapeutic efficacy; explore new technologies and approaches to better decipher the tumor microenvironment; and discuss strategies to intervene in the pro-tumor microenvironment and maximize therapeutic benefits.
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Affiliation(s)
- Yi Xiao
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Dihua Yu
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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171
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Liu Y, Yang M, Deng Y, Su G, Enninful A, Guo CC, Tebaldi T, Zhang D, Kim D, Bai Z, Norris E, Pan A, Li J, Xiao Y, Halene S, Fan R. High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue. Cell 2020; 183:1665-1681.e18. [PMID: 33188776 DOI: 10.1016/j.cell.2020.10.026] [Citation(s) in RCA: 372] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 07/31/2020] [Accepted: 10/14/2020] [Indexed: 12/21/2022]
Abstract
We present deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) for co-mapping of mRNAs and proteins in a formaldehyde-fixed tissue slide via next-generation sequencing (NGS). Parallel microfluidic channels were used to deliver DNA barcodes to the surface of a tissue slide, and crossflow of two sets of barcodes, A1-50 and B1-50, followed by ligation in situ, yielded a 2D mosaic of tissue pixels, each containing a unique full barcode AB. Application to mouse embryos revealed major tissue types in early organogenesis as well as fine features like microvasculature in a brain and pigmented epithelium in an eye field. Gene expression profiles in 10-μm pixels conformed into the clusters of single-cell transcriptomes, allowing for rapid identification of cell types and spatial distributions. DBiT-seq can be adopted by researchers with no experience in microfluidics and may find applications in a range of fields including developmental biology, cancer biology, neuroscience, and clinical pathology.
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Affiliation(s)
- Yang Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Mingyu Yang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Yanxiang Deng
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Graham Su
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Cindy C Guo
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Toma Tebaldi
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA; Section of Hematology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA
| | - Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Dongjoo Kim
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Eileen Norris
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Alisia Pan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Jiatong Li
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Yang Xiao
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Stephanie Halene
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA; Section of Hematology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA; Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT 06520, USA.
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172
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Gastman B, Agarwal PK, Berger A, Boland G, Broderick S, Butterfield LH, Byrd D, Fecci PE, Ferris RL, Fong Y, Goff SL, Grabowski MM, Ito F, Lim M, Lotze MT, Mahdi H, Malafa M, Morris CD, Murthy P, Neves RI, Odunsi A, Pai SI, Prabhakaran S, Rosenberg SA, Saoud R, Sethuraman J, Skitzki J, Slingluff CL, Sondak VK, Sunwoo JB, Turcotte S, Yeung CC, Kaufman HL. Defining best practices for tissue procurement in immuno-oncology clinical trials: consensus statement from the Society for Immunotherapy of Cancer Surgery Committee. J Immunother Cancer 2020; 8:e001583. [PMID: 33199512 PMCID: PMC7670953 DOI: 10.1136/jitc-2020-001583] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2020] [Indexed: 12/11/2022] Open
Abstract
Immunotherapy is now a cornerstone for cancer treatment, and much attention has been placed on the identification of prognostic and predictive biomarkers. The success of biomarker development is dependent on accurate and timely collection of biospecimens and high-quality processing, storage and shipping. Tumors are also increasingly used as source material for the generation of therapeutic T cells. There have been few guidelines or consensus statements on how to optimally collect and manage biospecimens and source material being used for immunotherapy and related research. The Society for Immunotherapy of Cancer Surgery Committee has brought together surgical experts from multiple subspecialty disciplines to identify best practices and to provide consensus on how best to access and manage specific tissues for immuno-oncology treatments and clinical investigation. In addition, the committee recommends early integration of surgeons and other interventional physicians with expertise in biospecimen collection, especially in clinical trials, to optimize the quality of tissue and the validity of correlative clinical studies in cancer immunotherapy.
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Affiliation(s)
- Brian Gastman
- Department of Plastic Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Piyush K Agarwal
- Department of Surgery, University of Chicago, Chicago, Illinois, USA
| | - Adam Berger
- Division of Surgical Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey, USA
| | - Genevieve Boland
- Department of Surgical Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Stephen Broderick
- Oncology, Johns Hopkins Medicine Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
- Department of Surgery, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Lisa H Butterfield
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
- Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
| | - David Byrd
- Department of Surgery, University of Washington, Seattle, Washington, USA
| | - Peter E Fecci
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - Robert L Ferris
- Departments of Otolaryngology, Immunology, and Radiation Oncology, University of Pittsburgh Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
| | - Yuman Fong
- Department of Surgery, City of Hope National Medical Center, Duarte, California, USA
| | | | - Matthew M Grabowski
- Department of Neurosurgery, Duke Center for Brain and Spine Metastasis, Durham, North Carolina, USA
| | - Fumito Ito
- Center for Immunotherapy, Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Michael Lim
- Departments of Neurosurgery, Oncology, Radiation Oncology, and Otolaryngology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael T Lotze
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Haider Mahdi
- OBGYN and Women's Health Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mokenge Malafa
- Department of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Carol D Morris
- Division of Orthopaedic Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Pranav Murthy
- Department of Surgery, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Rogerio I Neves
- Department of Surgery, Penn State Cancer Institute, Hershey, Pennsylvania, USA
| | - Adekunle Odunsi
- Departments of Immunology and Gynecologic Oncology, Roswell Park Cancer Institute, Buffalo, New York, USA
| | - Sara I Pai
- Department of Surgical Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sangeetha Prabhakaran
- Division of Surgical Oncology, Department of Surgery, UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA
| | | | - Ragheed Saoud
- Department of Surgery, University of Chicago Hospitals, Chicago, Illinois, United States
| | | | - Joseph Skitzki
- Departments of Surgical Oncology and Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Craig L Slingluff
- Department of Surgery, Division of Surgical Oncology, Breast and Melanoma Surgery, University of Virginia, Charlottesville, Virginia, USA
| | - Vernon K Sondak
- Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | - John B Sunwoo
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, California, USA
| | - Simon Turcotte
- Surgery Department, Centre Hospitalier de l'Universite de Montreal, Montreal, Quebec, Canada
| | - Cecilia Cs Yeung
- Department of Pathology, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Howard L Kaufman
- Department of Surgical Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Immuneering Corp, Cambridge, Massachusetts, USA
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173
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Zhang K, Deng R, Gao H, Teng X, Li J. Lighting up single-nucleotide variation in situ in single cells and tissues. Chem Soc Rev 2020; 49:1932-1954. [PMID: 32108196 DOI: 10.1039/c9cs00438f] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ability to 'see' genetic information directly in single cells can provide invaluable insights into complex biological systems. In this review, we discuss recent advances of in situ imaging technologies for visualizing the subtlest sequence alteration, single-nucleotide variation (SNV), at single-cell level. The mechanism of recently developed methods for SNV discrimination are summarized in detail. With recent developments, single-cell SNV imaging methods have opened a new door for studying the heterogenous and stochastic genetic information in individual cells. Furthermore, SNV imaging can be used on morphologically preserved tissue, which can provide information on histological context for gene expression profiling in basic research and genetic diagnosis. Moreover, the ability to visualize SNVs in situ can be further developed into in situ sequencing technology. We expect this review to inspire more research work into in situ SNV imaging technologies for investigating cellular phenotypes and gene regulation at single-nucleotide resolution, and developing new clinical and biomedical applications.
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Affiliation(s)
- Kaixiang Zhang
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China. and School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Ruijie Deng
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China.
| | - Hua Gao
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China. and Department of Pathogeny Biology, Medical College, Zhengzhou University, Zhengzhou 450001, China
| | - Xucong Teng
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China.
| | - Jinghong Li
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China.
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174
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Miedema A, Wijering MHC, Eggen BJL, Kooistra SM. High-Resolution Transcriptomic and Proteomic Profiling of Heterogeneity of Brain-Derived Microglia in Multiple Sclerosis. Front Mol Neurosci 2020; 13:583811. [PMID: 33192299 PMCID: PMC7654237 DOI: 10.3389/fnmol.2020.583811] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/23/2020] [Indexed: 12/12/2022] Open
Abstract
Microglia are important for central nervous system (CNS) homeostasis and first to respond to tissue damage and perturbations. Microglia are heterogeneous cells; in case of pathology, microglia adopt a range of phenotypes with altered functions. However, how these different microglia subtypes are implicated in CNS disease is largely unresolved. Multiple sclerosis (MS) is a chronic demyelinating disease of the CNS, characterized by inflammation and axonal degeneration, ultimately leading to neurological decline. One way microglia are implicated in MS is through stimulation of remyelination. They facilitate efficient remyelination by phagocytosis of myelin debris. In addition, microglia recruit oligodendrocyte precursor cells (OPCs) to demyelinated areas and stimulate remyelination. The development of high-resolution technologies to profile individual cells has greatly contributed to our understanding of microglia heterogeneity and function under normal and pathological conditions. Gene expression profiling technologies have evolved from whole tissue RNA sequencing toward single-cell or nucleus sequencing. Single microglia proteomic profiles are also increasingly generated, offering another layer of high-resolution data. Here, we will review recent studies that have employed these technologies in the context of MS and their respective advantages and disadvantages. Moreover, recent developments that allow for (single) cell profiling while retaining spatial information and tissue context will be discussed.
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Affiliation(s)
- Anneke Miedema
- Section Molecular Neurobiology, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Marion H C Wijering
- Section Molecular Neurobiology, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Bart J L Eggen
- Section Molecular Neurobiology, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Susanne M Kooistra
- Section Molecular Neurobiology, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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175
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Yeldell SB, Yang L, Lee J, Eberwine JH, Dmochowski IJ. Oligonucleotide Probe for Transcriptome in Vivo Analysis (TIVA) of Single Neurons with Minimal Background. ACS Chem Biol 2020; 15:2714-2721. [PMID: 32902259 DOI: 10.1021/acschembio.0c00499] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Messenger RNA (mRNA) isolated from single cells can generate powerful biological insights, including the discovery of new cell types with unique functions as well as markers potentially predicting a cell's response to various therapeutic agents. We previously introduced an oligonucleotide-based technique for site-selective, photoinduced biotinylation and capture of mRNA within a living cell called transcriptome in vivo analysis (TIVA). Successful application of the TIVA technique hinges upon its oligonucleotide probe remaining completely inert (or "caged") to mRNA unless photoactivated. To improve the reliability of TIVA probe caging in diverse and challenging biological conditions, we applied a rational design process involving iterative modifications to the oligonucleotide construct. In this work, we discuss these design motivations and present an optimized probe with minimal background binding to mRNA prior to photolysis. We assess its caging performance through multiple in vitro assays including FRET analysis, native gel comigration, and pull down with model mRNA transcripts. Finally, we demonstrate that this improved probe can also isolate mRNA from single living neurons in brain tissue slices with excellent caging control.
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Affiliation(s)
- Sean B. Yeldell
- Department of Chemistry, University of Pennsylvania, 231 South 34 Street, Philadelphia, Pennsylvania 19104-6323, United States
| | - Linlin Yang
- Department of Chemistry, University of Pennsylvania, 231 South 34 Street, Philadelphia, Pennsylvania 19104-6323, United States
| | - Jaehee Lee
- Department of Pharmacology, University of Pennsylvania, 38 John Morgan Building, 3620 Hamilton Walk, Philadelphia, Pennsylvania 19104-6084, United States
| | - James H. Eberwine
- Department of Pharmacology, University of Pennsylvania, 38 John Morgan Building, 3620 Hamilton Walk, Philadelphia, Pennsylvania 19104-6084, United States
| | - Ivan J. Dmochowski
- Department of Chemistry, University of Pennsylvania, 231 South 34 Street, Philadelphia, Pennsylvania 19104-6323, United States
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176
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Abstract
RNA movements and localization pervade biology, from embryonic development to disease. To identify RNAs at specific locations, we developed a strategy in which a uridine-adding enzyme is anchored to subcellular sites, where it directly marks RNAs with 3' terminal uridines. This localized RNA recording approach yields a record of RNA locations, and is validated through identification of RNAs localized selectively to the endoplasmic reticulum (ER) or mitochondria. We identify a broad dual localization pattern conserved from yeast to human cells, in which the same battery of mRNAs encounter both ER and mitochondria in both species, and include an mRNA encoding a key stress sensor. Subunits of many multiprotein complexes localize to both the ER and mitochondria, suggesting coordinated assembly. Noncoding RNAs in the course of RNA surveillance and processing encounter both organelles. By providing a record of RNA locations over time, the approach complements those that capture snapshots of instantaneous positions.
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177
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Li K, Varden L, Henderson A, Lufkin T, Kraus P. Simultaneous detection of multiple mRNAs and proteins in bovine IVD cells and tissue with single cell resolution. Biotechnol Lett 2020; 43:13-24. [PMID: 32902710 DOI: 10.1007/s10529-020-02997-9] [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: 06/28/2020] [Accepted: 09/01/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Interactions of cells with their neighbors and influences by the surrounding extracellular matrix (ECM) is reflected in a cells transcriptome and proteome. In tissues comprised of heterogeneous cell populations or cells depending on ECM signalling cues such as those of the intervertebral disc (IVD), this information is obscured or lost when cells are pooled for the commonly used transcript analysis by quantitative PCR or RNA sequencing. Instead, these cells require means to analyse RNA transcript and protein distribution at a single cell or subcellular level to identify different cell types and functions, without removing them from their surrounding signalling cues. RESULTS We developed a simple, sequential protocol combining RNA is situ hybridisation (RISH) and immunohistochemistry (IHC) for the simultaneous analysis of multiple transcripts alongside proteins. This allows one to characterize heterogeneous cell populations at the single cell level in the natural cell environment and signalling context, both in vivo and in vitro. This protocol is demonstrated on cells of the bovine IVD, for transcripts and proteins involved in mechanotransduction, stemness and cell proliferation. CONCLUSIONS A simple, sequential protocol combining RISH and IHC is presented that allows for simultaneous information on RNA transcripts and proteins to characterize cells within a heterogeneous cell population and complex signalling environments such as those of the IVD.
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Affiliation(s)
- Kangning Li
- Department of Biology, Clarkson University, Potsdam, NY, USA
| | - Lara Varden
- Department of Biology, Clarkson University, Potsdam, NY, USA
| | | | - Thomas Lufkin
- Department of Biology, Clarkson University, Potsdam, NY, USA
| | - Petra Kraus
- Department of Biology, Clarkson University, Potsdam, NY, USA.
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178
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Adekunle DA, Wang ET. Transcriptome-wide organization of subcellular microenvironments revealed by ATLAS-Seq. Nucleic Acids Res 2020; 48:5859-5872. [PMID: 32421779 PMCID: PMC7293051 DOI: 10.1093/nar/gkaa334] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/22/2022] Open
Abstract
Subcellular organization of RNAs and proteins is critical for cell function, but we still lack global maps and conceptual frameworks for how these molecules are localized in cells and tissues. Here, we introduce ATLAS-Seq, which generates transcriptomes and proteomes from detergent-free tissue lysates fractionated across a sucrose gradient. Proteomic analysis of fractions confirmed separation of subcellular compartments. Unexpectedly, RNAs tended to co-sediment with other RNAs in similar protein complexes, cellular compartments, or with similar biological functions. With the exception of those encoding secreted proteins, most RNAs sedimented differently than their encoded protein counterparts. To identify RNA binding proteins potentially driving these patterns, we correlated their sedimentation profiles to all RNAs, confirming known interactions and predicting new associations. Hundreds of alternative RNA isoforms exhibited distinct sedimentation patterns across the gradient, despite sharing most of their coding sequence. These observations suggest that transcriptomes can be organized into networks of co-segregating mRNAs encoding functionally related proteins and provide insights into the establishment and maintenance of subcellular organization.
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Affiliation(s)
- Danielle A Adekunle
- Department of Molecular Genetics & Microbiology, UF Genetics Institute, Center for NeuroGenetics, University of Florida, USA.,Department of Biology, Massachusetts Institute of Technology, USA
| | - Eric T Wang
- Department of Molecular Genetics & Microbiology, UF Genetics Institute, Center for NeuroGenetics, University of Florida, USA
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179
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Fan J, Slowikowski K, Zhang F. Single-cell transcriptomics in cancer: computational challenges and opportunities. Exp Mol Med 2020; 52:1452-1465. [PMID: 32929226 PMCID: PMC8080633 DOI: 10.1038/s12276-020-0422-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/26/2020] [Accepted: 03/10/2020] [Indexed: 02/07/2023] Open
Abstract
Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.
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Affiliation(s)
- Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Kamil Slowikowski
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, USA
| | - Fan Zhang
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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180
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Savulescu AF, Jacobs C, Negishi Y, Davignon L, Mhlanga MM. Pinpointing Cell Identity in Time and Space. Front Mol Biosci 2020; 7:209. [PMID: 32923457 PMCID: PMC7456825 DOI: 10.3389/fmolb.2020.00209] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/30/2020] [Indexed: 01/15/2023] Open
Abstract
Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.
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Affiliation(s)
- Anca F. Savulescu
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Caron Jacobs
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape Town, Cape Town, South Africa
| | - Yutaka Negishi
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Laurianne Davignon
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Musa M. Mhlanga
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape Town, Cape Town, South Africa
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
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181
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Sun YM, Chen YQ. Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application. J Hematol Oncol 2020; 13:109. [PMID: 32778133 PMCID: PMC7416809 DOI: 10.1186/s13045-020-00945-8] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/27/2020] [Indexed: 12/20/2022] Open
Abstract
Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.
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Affiliation(s)
- Yu-Meng Sun
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275 People’s Republic of China
| | - Yue-Qin Chen
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275 People’s Republic of China
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182
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Teves JM, Won KJ. Mapping Cellular Coordinates through Advances in Spatial Transcriptomics Technology. Mol Cells 2020; 43:591-599. [PMID: 32507771 PMCID: PMC7398793 DOI: 10.14348/molcells.2020.0020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 04/22/2020] [Accepted: 05/10/2020] [Indexed: 12/13/2022] Open
Abstract
Complex cell-to-cell communication underlies the basic processes essential for homeostasis in the given tissue architecture. Obtaining quantitative gene-expression of cells in their native context has significantly advanced through single-cell RNA sequencing technologies along with mechanical and enzymatic tissue manipulation. This approach, however, is largely reliant on the physical dissociation of individual cells from the tissue, thus, resulting in a library with unaccounted positional information. To overcome this, positional information can be obtained by integrating imaging and positional barcoding. Collectively, spatial transcriptomics strategies provide tissue architecture-dependent as well as position-dependent cellular functions. This review discusses the current technologies for spatial transcriptomics ranging from the methods combining mechanical dissociation and single-cell RNA sequencing to computational spatial re-mapping.
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Affiliation(s)
- Joji Marie Teves
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, DK-2200 Copenhagen, Denmark
- Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), Faculty of Health Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, DK-00 Copenhagen, Denmark
| | - Kyoung Jae Won
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, DK-2200 Copenhagen, Denmark
- Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), Faculty of Health Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, DK-00 Copenhagen, Denmark
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183
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Zhou Y, Jia E, Pan M, Zhao X, Ge Q. Encoding Method of Single-cell Spatial Transcriptomics Sequencing. Int J Biol Sci 2020; 16:2663-2674. [PMID: 32792863 PMCID: PMC7415427 DOI: 10.7150/ijbs.43887] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 07/20/2020] [Indexed: 12/11/2022] Open
Abstract
Despite significant advances in parallel single-cell RNA sequencing revealing astonishing cellular heterogeneity in many tissue types, the spatial information in the tissue context remains missing. Spatial transcriptome sequencing technology is designed to distinguish the gene expression of individual cells in their original location. The technology is important for the identification of tissue function, tracking developmental processes, and pathological and molecular detection. Encoding the position information is the key to spatial transcriptomics because different methods have different encoding efficiencies and application scenarios. In this review, we focus on the latest technologies of single-cell spatial transcriptomics, including technologies based on microwell plates, barcoded bead arrays, microdissection, in situ hybridization, and barcode in situ targeting, as well as mixed separation-based technologies. Moreover, we compare these encoding methods for use as a reference when choosing the appropriate technology.
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Affiliation(s)
- Ying Zhou
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Erteng Jia
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Min Pan
- School of Medicine, Southeast University, Nanjing 210097, China
| | - Xiangwei Zhao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Qinyu Ge
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
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184
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Characterizing the ecological and evolutionary dynamics of cancer. Nat Genet 2020; 52:759-767. [DOI: 10.1038/s41588-020-0668-4] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 06/22/2020] [Indexed: 12/14/2022]
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185
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de Vries NL, Mahfouz A, Koning F, de Miranda NFCC. Unraveling the Complexity of the Cancer Microenvironment With Multidimensional Genomic and Cytometric Technologies. Front Oncol 2020; 10:1254. [PMID: 32793500 PMCID: PMC7390924 DOI: 10.3389/fonc.2020.01254] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 06/17/2020] [Indexed: 12/26/2022] Open
Abstract
Cancers are characterized by extensive heterogeneity that occurs intratumorally, between lesions, and across patients. To study cancer as a complex biological system, multidimensional analyses of the tumor microenvironment are paramount. Single-cell technologies such as flow cytometry, mass cytometry, or single-cell RNA-sequencing have revolutionized our ability to characterize individual cells in great detail and, with that, shed light on the complexity of cancer microenvironments. However, a key limitation of these single-cell technologies is the lack of information on spatial context and multicellular interactions. Investigating spatial contexts of cells requires the incorporation of tissue-based techniques such as multiparameter immunofluorescence, imaging mass cytometry, or in situ detection of transcripts. In this Review, we describe the rise of multidimensional single-cell technologies and provide an overview of their strengths and weaknesses. In addition, we discuss the integration of transcriptomic, genomic, epigenomic, proteomic, and spatially-resolved data in the context of human cancers. Lastly, we will deliberate on how the integration of multi-omics data will help to shed light on the complex role of cell types present within the human tumor microenvironment, and how such system-wide approaches may pave the way toward more effective therapies for the treatment of cancer.
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Affiliation(s)
- Natasja L. de Vries
- Pathology, Leiden University Medical Center, Leiden, Netherlands
- Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, Netherlands
| | - Ahmed Mahfouz
- Human Genetics, Leiden University Medical Center, Leiden, Netherlands
- Delft Bioinformatics Laboratory, Delft University of Technology, Delft, Netherlands
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands
| | - Frits Koning
- Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, Netherlands
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186
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Huang L, Kebschull JM, Fürth D, Musall S, Kaufman MT, Churchland AK, Zador AM. BRICseq Bridges Brain-wide Interregional Connectivity to Neural Activity and Gene Expression in Single Animals. Cell 2020; 182:177-188.e27. [PMID: 32619423 PMCID: PMC7771207 DOI: 10.1016/j.cell.2020.05.029] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 03/27/2020] [Accepted: 05/15/2020] [Indexed: 12/26/2022]
Abstract
Comprehensive analysis of neuronal networks requires brain-wide measurement of connectivity, activity, and gene expression. Although high-throughput methods are available for mapping brain-wide activity and transcriptomes, comparable methods for mapping region-to-region connectivity remain slow and expensive because they require averaging across hundreds of brains. Here we describe BRICseq (brain-wide individual animal connectome sequencing), which leverages DNA barcoding and sequencing to map connectivity from single individuals in a few weeks and at low cost. Applying BRICseq to the mouse neocortex, we find that region-to-region connectivity provides a simple bridge relating transcriptome to activity: the spatial expression patterns of a few genes predict region-to-region connectivity, and connectivity predicts activity correlations. We also exploited BRICseq to map the mutant BTBR mouse brain, which lacks a corpus callosum, and recapitulated its known connectopathies. BRICseq allows individual laboratories to compare how age, sex, environment, genetics, and species affect neuronal wiring and to integrate these with functional activity and gene expression.
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Affiliation(s)
- Longwen Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Justus M Kebschull
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Daniel Fürth
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Simon Musall
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Matthew T Kaufman
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637, USA; Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL 60637, USA
| | | | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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187
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Kucinski I, Gottgens B. Advancing Stem Cell Research through Multimodal Single-Cell Analysis. Cold Spring Harb Perspect Biol 2020; 12:a035725. [PMID: 31932320 PMCID: PMC7328456 DOI: 10.1101/cshperspect.a035725] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Technological advances play a key role in furthering our understanding of stem cell biology, and advancing the prospects of regenerative therapies. Highly parallelized methods, developed in the last decade, can profile DNA, RNA, or proteins in thousands of cells and even capture data across two or more modalities (multiomics). This allows unbiased and precise definition of molecular cell states, thus allowing classification of cell types, tracking of differentiation trajectories, and discovery of underlying mechanisms. Despite being based on destructive techniques, novel experimental and bioinformatic approaches enable embedding and extraction of temporal information, which is essential for deconvolution of complex data and establishing cause and effect relationships. Here, we provide an overview of recent studies pertinent to stem cell biology, followed by an outlook on how further advances in single-cell molecular profiling and computational analysis have the potential to shape the future of both basic and translational research.
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Affiliation(s)
- Iwo Kucinski
- Wellcome-MRC Cambridge Stem Cell Institute and Department of Haematology, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge CB2 0AW, United Kingdom
| | - Berthold Gottgens
- Wellcome-MRC Cambridge Stem Cell Institute and Department of Haematology, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge CB2 0AW, United Kingdom
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188
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For the Greater (Bacterial) Good: Heterogeneous Expression of Energetically Costly Virulence Factors. Infect Immun 2020; 88:IAI.00911-19. [PMID: 32041785 DOI: 10.1128/iai.00911-19] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Bacterial populations are phenotypically heterogeneous, which allows subsets of cells to survive and thrive following changes in environmental conditions. For bacterial pathogens, changes within the host environment occur over the course of the immune response to infection and can result in exposure to host-derived, secreted antimicrobials or force direct interactions with immune cells. Many recent studies have shown host cell interactions promote virulence factor expression, forcing subsets of bacterial cells to battle the host response, while other bacteria reap the benefits of this pacification. It still remains unclear whether virulence factor expression is truly energetically costly within host tissues and whether expression is sufficient to impact the growth kinetics of virulence factor-expressing cells. However, it is clear that slow-growing subsets of bacteria emerge during infection and that these subsets are particularly difficult to eliminate with antibiotics. This minireview will focus on our current understanding of heterogenous virulence factor expression and discuss the evidence that supports or refutes the hypothesis that virulence factor expression is linked to slowed growth and antibiotic tolerance.
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189
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Liao J, Lu X, Shao X, Zhu L, Fan X. Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics. Trends Biotechnol 2020; 39:43-58. [PMID: 32505359 DOI: 10.1016/j.tibtech.2020.05.006] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 01/17/2023]
Abstract
Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.
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Affiliation(s)
- Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Ling Zhu
- The Save Sight Institute, Faculty of Medicine and Health, the University of Sydney, Sydney, NSW 2000, Australia
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; The Save Sight Institute, Faculty of Medicine and Health, the University of Sydney, Sydney, NSW 2000, Australia.
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190
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Bortolomeazzi M, Keddar MR, Ciccarelli FD, Benedetti L. Identification of non-cancer cells from cancer transcriptomic data. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2020; 1863:194445. [PMID: 31654804 PMCID: PMC7346884 DOI: 10.1016/j.bbagrm.2019.194445] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/20/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Michele Bortolomeazzi
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK
| | - Mohamed Reda Keddar
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK.
| | - Lorena Benedetti
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK.
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191
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Hasle N, Cooke A, Srivatsan S, Huang H, Stephany JJ, Krieger Z, Jackson D, Tang W, Pendyala S, Monnat RJ, Trapnell C, Hatch EM, Fowler DM. High-throughput, microscope-based sorting to dissect cellular heterogeneity. Mol Syst Biol 2020; 16:e9442. [PMID: 32500953 PMCID: PMC7273721 DOI: 10.15252/msb.20209442] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 04/23/2020] [Accepted: 04/29/2020] [Indexed: 12/24/2022] Open
Abstract
Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single-cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.
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Affiliation(s)
- Nicholas Hasle
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | | | - Sanjay Srivatsan
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | - Heather Huang
- Divisions of Basic Sciences and Human BiologyFred Hutchinson Cancer Research CenterSeattleWAUSA
| | - Jason J Stephany
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | - Zachary Krieger
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | - Dana Jackson
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | - Weiliang Tang
- Department of PathologyUniversity of WashingtonSeattleWAUSA
| | - Sriram Pendyala
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | - Raymond J Monnat
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
- Department of PathologyUniversity of WashingtonSeattleWAUSA
| | - Cole Trapnell
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | - Emily M Hatch
- Divisions of Basic Sciences and Human BiologyFred Hutchinson Cancer Research CenterSeattleWAUSA
| | - Douglas M Fowler
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
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192
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Nagarajan MB, Tentori AM, Zhang WC, Slack FJ, Doyle PS. Spatially resolved and multiplexed MicroRNA quantification from tissue using nanoliter well arrays. MICROSYSTEMS & NANOENGINEERING 2020; 6:51. [PMID: 32419951 PMCID: PMC7211184 DOI: 10.1038/s41378-020-0169-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 04/08/2020] [Accepted: 04/09/2020] [Indexed: 05/27/2023]
Abstract
Spatially resolved gene expression patterns are emerging as a key component of medical studies, including companion diagnostics, but technologies for quantification and multiplexing are limited. We present a method to perform spatially resolved and multiplexed microRNA (miRNA) measurements from formalin-fixed, paraffin-embedded (FFPE) tissue. Using nanoliter well arrays to pixelate the tissue section and photopatterned hydrogels to quantify miRNA, we identified differentially expressed miRNAs in tumors from a genetically engineered mouse model for non-small cell lung cancer (K-rasLSL-G12D/+; p53fl/fl). This technology could be used to quantify heterogeneities in tissue samples and lead to informed, biomarker-based diagnostics.
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Affiliation(s)
- Maxwell B. Nagarajan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Augusto M. Tentori
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Wen Cai Zhang
- HMS Initiative for RNA Medicine, Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA 02215 USA
| | - Frank J. Slack
- HMS Initiative for RNA Medicine, Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA 02215 USA
| | - Patrick S. Doyle
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
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193
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Assmus AM, Mullins JJ, Brown CM, Mullins LJ. Cellular plasticity: A mechanism for homeostasis in the kidney. Acta Physiol (Oxf) 2020; 229:e13447. [PMID: 31991057 DOI: 10.1111/apha.13447] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 01/15/2020] [Accepted: 01/24/2020] [Indexed: 12/30/2022]
Abstract
Cellular plasticity is a topical subject with interest spanning a wide range of fields from developmental biology to regenerative medicine. Even the nomenclature is a subject of debate, and the underlying mechanisms are still under investigation. On top of injury repair, cell plasticity is a constant physiological process in adult organisms and tissues, in response to homeostatic challenges. In this review we discuss two examples of plasticity for the maintenance of homeostasis in the renal system-namely the renin-producing juxtaglomerular cells (JG cells) and cortical collecting duct (CCD) cells. JG cells show plasticity through recruitment mechanisms, answering the demand for an increase in renin production. In the CCD, cells appear to have the ability to transdifferentiate between principal and intercalated cells to help maintain the highly regulated solute transport levels of that segment. These two cases highlight the complexity of plasticity processes and the role they can play in the kidney.
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Affiliation(s)
- Adrienne M. Assmus
- The University of Edinburgh ‐ Cardiovascular Science (CVS) Queen's Medical Research Institute Edinburgh Scotland UK
| | - John J. Mullins
- The University of Edinburgh ‐ Cardiovascular Science (CVS) Queen's Medical Research Institute Edinburgh Scotland UK
| | - Cara M. Brown
- The University of Edinburgh ‐ Cardiovascular Science (CVS) Queen's Medical Research Institute Edinburgh Scotland UK
| | - Linda J. Mullins
- The University of Edinburgh ‐ Cardiovascular Science (CVS) Queen's Medical Research Institute Edinburgh Scotland UK
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194
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Wilbrey-Clark A, Roberts K, Teichmann SA. Cell Atlas technologies and insights into tissue architecture. Biochem J 2020; 477:1427-1442. [PMID: 32339226 PMCID: PMC7200628 DOI: 10.1042/bcj20190341] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 03/21/2020] [Accepted: 03/24/2020] [Indexed: 12/17/2022]
Abstract
Since Robert Hooke first described the existence of 'cells' in 1665, scientists have sought to identify and further characterise these fundamental units of life. While our understanding of cell location, morphology and function has expanded greatly; our understanding of cell types and states at the molecular level, and how these function within tissue architecture, is still limited. A greater understanding of our cells could revolutionise basic biology and medicine. Atlasing initiatives like the Human Cell Atlas aim to identify all cell types at the molecular level, including their physical locations, and to make this reference data openly available to the scientific community. This is made possible by a recent technology revolution: both in single-cell molecular profiling, particularly single-cell RNA sequencing, and in spatially resolved methods for assessing gene and protein expression. Here, we review available and upcoming atlasing technologies, the biological insights gained to date and the promise of this field for the future.
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195
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Wang Y, Mashock M, Tong Z, Mu X, Chen H, Zhou X, Zhang H, Zhao G, Liu B, Li X. Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology. Front Oncol 2020; 10:447. [PMID: 32328458 PMCID: PMC7160325 DOI: 10.3389/fonc.2020.00447] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 03/13/2020] [Indexed: 12/20/2022] Open
Abstract
RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.
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Affiliation(s)
- Ye Wang
- Clinical Laboratory, Qingdao Central Hospital, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Michael Mashock
- Department of Pathology & Laboratory Medicine, UCLA Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
| | - Zhuang Tong
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xiaofeng Mu
- Clinical Laboratory, Qingdao Central Hospital, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hong Chen
- Qiqihaer First Hospital, Qiqihar, China
| | - Xin Zhou
- Qiqihaer First Hospital, Qiqihar, China
| | - Hong Zhang
- Department of Pathology & Laboratory Medicine, UCLA Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
| | - Gexin Zhao
- Department of Pathology & Laboratory Medicine, UCLA Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
| | - Bin Liu
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xinmin Li
- Department of Pathology & Laboratory Medicine, UCLA Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
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196
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Abstract
Current methods for chromosome painting via fluorescence in situ hybridization (FISH) are costly, time-consuming, and limited in complexity. In contrast to conventional sources of probe, Oligopaints are computationally designed, synthesized on microarrays, and amplified by PCR. This approach allows for precise control over the sequences they target, which can range from a few kilobases to entire chromosomes with the same basic protocol. We have utilized the flexibility and scalability of Oligopaints to generate low-cost and renewable chromosome paints for Drosophila, mouse, and human chromosomes. These Oligopaint libraries can be customized to label any genomic feature(s) in a chromosome-wide manner. Additionally, this method is compatible with sequential FISH to label entire genomes with a single denaturation step. Here, we outline a protocol and considerations to scale the Oligopaint technology for fluorescent labeling of whole chromosomes.
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197
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Kagan J, Moritz RL, Mazurchuk R, Lee JH, Kharchenko PV, Rozenblatt-Rosen O, Ruppin E, Edfors F, Ginty F, Goltsev Y, Wells JA, LaCava J, Riesterer JL, Germain RN, Shi T, Chee MS, Budnik BA, Yates JR, Chait BT, Moffitt JR, Smith RD, Srivastava S. National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions. J Proteome Res 2020; 19:1900-1912. [PMID: 32163288 DOI: 10.1021/acs.jproteome.0c00021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts' opinion on the development and application of multiomic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, and prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular, and functional aberrations within the lesion and within individual cells. Single-cell proteomic data will be essential for the establishment of new tools with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included (i) The best way/s to analyze single-cells from fresh and preserved tissue; (ii) Detection and analysis of secreted molecules and from single cells, especially from a tissue slice; (iii) Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment; (iv) Multiomic integration of data to support and inform proteomic measurements; (v) Subcellular organelles-identifying abnormal structure, function, distribution, and location within individual premalignant and malignant cells; (vi) How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post-translational modifications (PTM); (vii) The depth of coverage measured concurrently using single-cell techniques; (viii) Quantitation - absolute or semiquantitative? (ix) Single methodology or multiplexed combinations? (x) Application of analytical methods for identification of biologically significant subsets; (xi) Data visualization of N-dimensional data sets; (xii) How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME); (xiii) Associations between intrinsic cellular processes and extrinsic stimuli; (xiv) How to predict cellular responses to stress-inducing stimuli; (xv) Identification of new markers for prediction of progression from precursor, benign, and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells; (xvi) Identification of new targets for immunoprevention or immunotherapy-identification of neoantigens and surfactome of individual cells within a lesion.
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Affiliation(s)
- Jacob Kagan
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington, United States
| | - Richard Mazurchuk
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States
| | - Je Hyuk Lee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States
| | - Peter Vasili Kharchenko
- Blavatnik Institute for Biomedical Information, Harvard Medical School, Boston, Massachusetts, United States
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, United States
| | - Fredrik Edfors
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Fiona Ginty
- Life Sciences and Molecular Diagnostics Laboratory, GE Global Research Center, Niskayuna, New York, United States
| | - Yury Goltsev
- Department of Microbiology and Immunology, Baxter Laboratory in Stem Cell Biology, Stanford University, Stanford Medical School, Stanford, California, United States
| | - James A Wells
- Department of Pharmaceutical Sciences, University of California, San Francisco, California, United States
| | - John LaCava
- Laboratory of Cellular and Structural Biology, Rockefeller University, New York, New York, United States
| | - Jessica L Riesterer
- Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, Oregon, United States
| | - Ronald N Germain
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, Maryland, United States
| | - Tujin Shi
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Mark S Chee
- Encodia, Inc., San Diego, California, United States
| | - Bogdan A Budnik
- Faculty of Arts & Sciences, Division of Science. Harvard University, Boston, Massachusetts, United States
| | - John R Yates
- Department of Molecular Medicine, Scripps Research Institute, La Jolla, California, United States
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York, United States
| | - Jeffery R Moffitt
- Boston Children's Hospital and Harvard University Medical School, Boston, Massachusetts, United States
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Sudhir Srivastava
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States
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198
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Museum Epigenomics: Charting the Future by Unlocking the Past. Trends Ecol Evol 2020; 35:295-300. [DOI: 10.1016/j.tree.2019.12.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 12/05/2019] [Accepted: 12/13/2019] [Indexed: 02/03/2023]
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199
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Moshkovskii SA, Lobas AA, Gorshkov MV. Single Cell Proteogenomics - Immediate Prospects. BIOCHEMISTRY (MOSCOW) 2020; 85:140-146. [PMID: 32093591 DOI: 10.1134/s0006297920020029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Recent technical advances in genomic technology have led to the explosive growth of transcriptome-wide studies at the level of single cells. The review describes the first steps of the single cell proteomics that has originated soon after development of transcriptomics methods. The first studies on the shotgun proteomics of single cells that used liquid chromatography/mass spectrometry have been already published. In these works, the cells were separated by the methods used in transcriptomics studies (e.g., cell sorting) and analyzed by modified mass spectrometry with tandem mass tags. The new proteogenomics approach involving integration of single cell transcriptomics and proteomics data will provide better understanding of the mechanisms of cell interactions in normal development and disease.
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Affiliation(s)
- S A Moshkovskii
- Pirogov Russian National Research Medical University, Moscow, 117997, Russia. .,Orekhovich Institute of Biomedical Chemistry, Moscow, 119121, Russia
| | - A A Lobas
- Talrose Institute for Energy Problems of Chemical Physics, Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | - M V Gorshkov
- Talrose Institute for Energy Problems of Chemical Physics, Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
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200
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Chavkin NW, Hirschi KK. Single Cell Analysis in Vascular Biology. Front Cardiovasc Med 2020; 7:42. [PMID: 32296715 PMCID: PMC7137757 DOI: 10.3389/fcvm.2020.00042] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/05/2020] [Indexed: 12/12/2022] Open
Abstract
The ability to quantify DNA, RNA, and protein variations at the single cell level has revolutionized our understanding of cellular heterogeneity within tissues. Via such analyses, individual cells within populations previously thought to be homogeneous can now be delineated into specific subpopulations expressing unique sets of genes, enabling specialized functions. In vascular biology, studies using single cell RNA sequencing have revealed extensive heterogeneity among endothelial and mural cells even within the same vessel, key intermediate cell types that arise during blood and lymphatic vessel development, and cell-type specific responses to disease. Thus, emerging new single cell analysis techniques are enabling vascular biologists to elucidate mechanisms of vascular development, homeostasis, and disease that were previously not possible. In this review, we will provide an overview of single cell analysis methods and highlight recent advances in vascular biology made possible through single cell RNA sequencing.
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Affiliation(s)
- Nicholas W Chavkin
- Department of Cell Biology, Developmental Genomics Center, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Karen K Hirschi
- Department of Cell Biology, Developmental Genomics Center, School of Medicine, University of Virginia, Charlottesville, VA, United States.,Departments of Medicine and Genetics, Cardiovascular Research Center, School of Medicine, Yale University, New Haven, CT, United States
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