1
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Recalde A, Abdul-Nabi J, Junker P, van der Does C, Elsässer J, van Wolferen M, Albers SV. The use of thermostable fluorescent proteins for live imaging in Sulfolobus acidocaldarius. Front Microbiol 2024; 15:1445186. [PMID: 39314874 PMCID: PMC11416942 DOI: 10.3389/fmicb.2024.1445186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 08/19/2024] [Indexed: 09/25/2024] Open
Abstract
Introduction Among hyperthermophilic organisms, in vivo protein localization is challenging due to the high growth temperatures that can disrupt proper folding and function of mostly mesophilic-derived fluorescent proteins. While protein localization in the thermophilic model archaeon S. acidocaldarius has been achieved using antibodies with fluorescent probes in fixed cells, the use of thermostable fluorescent proteins for live imaging in thermophilic archaea has so far been unsuccessful. Given the significance of live protein localization in the field of archaeal cell biology, we aimed to identify fluorescent proteins for use in S. acidocaldarius. Methods We expressed various previously published and optimized thermostable fluorescent proteins along with fusion proteins of interest and analyzed the cells using flow cytometry and (thermo-) fluorescent microscopy. Results Of the tested proteins, thermal green protein (TGP) exhibited the brightest fluorescence when expressed in Sulfolobus cells. By optimizing the linker between TGP and a protein of interest, we could additionally successfully fuse proteins with minimal loss of fluorescence. TGP-CdvB and TGP-PCNA1 fusions displayed localization patterns consistent with previous immunolocalization experiments. Discussion These initial results in live protein localization in S. acidocaldarius at high temperatures, combined with recent advancements in thermomicroscopy, open new avenues in the field of archaeal cell biology. This progress finally enables localization experiments in thermophilic archaea, which have so far been limited to mesophilic organisms.
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Affiliation(s)
| | | | | | | | | | | | - Sonja-Verena Albers
- Molecular Biology of Archaea, Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
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2
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Gorin G, Carilli M, Chari T, Pachter L. Spectral neural approximations for models of transcriptional dynamics. Biophys J 2024; 123:2892-2901. [PMID: 38715358 PMCID: PMC11393700 DOI: 10.1016/j.bpj.2024.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/22/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024] Open
Abstract
The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single-species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, whereby solutions are approximated by 1) learning the weights of kernel distributions with constrained parameters or 2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression and parameter-scaled kernel weight regression, respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data.
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Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California
| | - Maria Carilli
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California.
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3
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Jia C, Grima R. Holimap: an accurate and efficient method for solving stochastic gene network dynamics. Nat Commun 2024; 15:6557. [PMID: 39095346 PMCID: PMC11297302 DOI: 10.1038/s41467-024-50716-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 07/13/2024] [Indexed: 08/04/2024] Open
Abstract
Gene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently predict how the distributions of gene product numbers vary across parameter space. To overcome these difficulties, here we present Holimap (high-order linear-mapping approximation), an approach that approximates the protein or mRNA number distributions of a complex gene regulatory network by the distributions of a much simpler reaction system. We demonstrate Holimap's computational advantages over conventional methods by applying it to predict the stochastic time-dependent dynamics of various gene networks, including transcriptional networks ranging from simple autoregulatory loops to complex randomly connected networks, post-transcriptional networks, and post-translational networks. Holimap is ideally suited to study how the intricate network of gene-gene interactions results in precise coordination and control of gene expression.
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Affiliation(s)
- Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing, China
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
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4
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Son JB, Kim S, Yang S, Ahn Y, Lee NK. Analysis of Fluorescent Proteins for Observing Single Gene Locus in a Live and Fixed Escherichia coli Cell. J Phys Chem B 2024; 128:6730-6741. [PMID: 38968413 PMCID: PMC11264270 DOI: 10.1021/acs.jpcb.4c01816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 07/07/2024]
Abstract
Fluorescent proteins (FPs) are essential tools for advanced microscopy techniques such as super-resolution imaging, single-particle tracking, and quantitative single-molecule counting. Various FPs fused to DNA-binding proteins have been used to observe the subcellular location and movement of specific gene loci in living and fixed bacterial cells. However, quantitative assessments of the properties of FPs for gene locus measurements are still lacking. Here, we assessed various FPs to observe specific gene loci in live and fixed Escherichia coli cells using a fluorescent repressor-operator binding system (FROS), tet operator-Tet repressor proteins (TetR). Tsr-fused FPs were used to assess the intensity and photostability of various FPs (five red FPs: mCherry2, FusionRed, mRFP, mCrimson3, and dKatushka; and seven yellow FPs: SYFP2, Venus, mCitrine, YPet, mClover3, mTopaz, and EYFP) at the single-molecule level in living cells. These FPs were then used for gene locus measurements using FROS. Our results indicate that TetR-mCrimson3 (red) and TetR-EYFP (yellow) had better properties for visualizing gene loci than the other TetR-FPs. Furthermore, fixation procedures affected the clustering of diffusing TetR-FPs and altered the locations of the TetR-FP foci. Fixation with formaldehyde consistently disrupted proper DNA locus observations using TetR-FPs. Notably, the foci measured using TetR-mCrimson3 remained close to their original positions in live cells after glyoxal fixation. This in vivo study provides a cell-imaging guide for the use of FPs for gene-locus observation in E. coli and a scheme for evaluating the use of FPs for other cell-imaging purposes.
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Affiliation(s)
| | | | | | - Youmin Ahn
- Department of Chemistry, Seoul
National University, 08826 Seoul, South
Korea
| | - Nam Ki Lee
- Department of Chemistry, Seoul
National University, 08826 Seoul, South
Korea
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5
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Chari T, Gorin G, Pachter L. Stochastic Modeling of Biophysical Responses to Perturbation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602131. [PMID: 39005347 PMCID: PMC11245117 DOI: 10.1101/2024.07.04.602131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the 'how' behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.
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Affiliation(s)
- Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
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6
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Zhang Z, Zabaikina I, Nieto C, Vahdat Z, Bokes P, Singh A. Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.28.601263. [PMID: 38979195 PMCID: PMC11230457 DOI: 10.1101/2024.06.28.601263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.
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Affiliation(s)
- Zhanhao Zhang
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
| | - Iryna Zabaikina
- Department of Applied Mathematics and Statistics, Comenius University, Bratislava 84248, Slovakia
| | - César Nieto
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
| | - Zahra Vahdat
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
| | - Pavol Bokes
- Department of Applied Mathematics and Statistics, Comenius University, Bratislava 84248, Slovakia
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
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7
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Nanda P, Barrere J, LaBar T, Murray AW. A dynamic network model predicts the phenotypes of multicellular clusters from cellular properties. Curr Biol 2024; 34:2672-2683.e4. [PMID: 38823384 DOI: 10.1016/j.cub.2024.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/20/2024] [Accepted: 05/08/2024] [Indexed: 06/03/2024]
Abstract
Cell division without cell separation produces multicellular clusters in budding yeast. Two fundamental characteristics of these clusters are their size (the number of cells per cluster) and cellular composition: the fractions of cells with different phenotypes. Using cells as nodes and links between mother and daughter cells as edges, we model cluster growth and breakage by varying three parameters: the cell division rate, the rate at which intercellular connections break, and the kissing number (the maximum number of connections to one cell). We find that the kissing number sets the maximum possible cluster size. Below this limit, the ratio of the cell division rate to the connection breaking rate determines the cluster size. If links have a constant probability of breaking per unit time, the probability that a link survives decreases exponentially with its age. Modeling this behavior recapitulates experimental data. We then use this framework to examine synthetic, differentiating clusters with two cell types, faster-growing germ cells and their somatic derivatives. The fraction of clusters that contain both cell types increases as either of two parameters increase: the kissing number and difference between the growth rate of germ and somatic cells. In a population of clusters, the variation in cellular composition is inversely correlated (r2 = 0.87) with the average fraction of somatic cells in clusters. Our results show how a small number of cellular features can control the phenotypes of multicellular clusters that were potentially the ancestors of more complex forms of multicellular development, organization, and reproduction.
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Affiliation(s)
- Piyush Nanda
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA; Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Julien Barrere
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Thomas LaBar
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Andrew W Murray
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
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8
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Hosseini SH, Roussel MR. Analytic delay distributions for a family of gene transcription models. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:6225-6262. [PMID: 39176425 DOI: 10.3934/mbe.2024273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Models intended to describe the time evolution of a gene network must somehow include transcription, the DNA-templated synthesis of RNA, and translation, the RNA-templated synthesis of proteins. In eukaryotes, the DNA template for transcription can be very long, often consisting of tens of thousands of nucleotides, and lengthy pauses may punctuate this process. Accordingly, transcription can last for many minutes, in some cases hours. There is a long history of introducing delays in gene expression models to take the transcription and translation times into account. Here we study a family of detailed transcription models that includes initiation, elongation, and termination reactions. We establish a framework for computing the distribution of transcription times, and work out these distributions for some typical cases. For elongation, a fixed delay is a good model provided elongation is fast compared to initiation and termination, and there are no sites where long pauses occur. The initiation and termination phases of the model then generate a nontrivial delay distribution, and elongation shifts this distribution by an amount corresponding to the elongation delay. When initiation and termination are relatively fast, the distribution of elongation times can be approximated by a Gaussian. A convolution of this Gaussian with the initiation and termination time distributions gives another analytic approximation to the transcription time distribution. If there are long pauses during elongation, because of the modularity of the family of models considered, the elongation phase can be partitioned into reactions generating a simple delay (elongation through regions where there are no long pauses), and reactions whose distribution of waiting times must be considered explicitly (initiation, termination, and motion through regions where long pauses are likely). In these cases, the distribution of transcription times again involves a nontrivial part and a shift due to fast elongation processes.
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Affiliation(s)
- S Hossein Hosseini
- Alberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Marc R Roussel
- Alberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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9
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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10
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Tang X, Wu Q, Shang L, Liu K, Ge Y, Liang P, Li B. Raman cell sorting for single-cell research. Front Bioeng Biotechnol 2024; 12:1389143. [PMID: 38832129 PMCID: PMC11145634 DOI: 10.3389/fbioe.2024.1389143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/08/2024] [Indexed: 06/05/2024] Open
Abstract
Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.
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Affiliation(s)
- Xusheng Tang
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qingyi Wu
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lindong Shang
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Kunxiang Liu
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yan Ge
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Liang
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
- Hooke Instruments Ltd., Changchun, China
| | - Bei Li
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
- University of Chinese Academy of Sciences, Beijing, China
- Hooke Instruments Ltd., Changchun, China
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11
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Miles CE, McKinley SA, Ding F, Lehoucq RB. Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions. Bull Math Biol 2024; 86:74. [PMID: 38740619 DOI: 10.1007/s11538-024-01301-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 04/20/2024] [Indexed: 05/16/2024]
Abstract
Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.
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Affiliation(s)
| | - Scott A McKinley
- Department of Mathematics, Tulane University, New Orleans, LA, USA
| | - Fangyuan Ding
- Departments of Biomedical Engineering, Developmental and Cell Biology, University of California, Irvine, Irvine, USA
| | - Richard B Lehoucq
- Discrete Math and Optimization, Sandia National Laboratories, Albuquerque, NM, USA
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12
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Lee U, Li C, Langer CB, Svetec N, Zhao L. Comparative Single Cell Analysis of Transcriptional Bursting Reveals the Role of Genome Organization on de novo Transcript Origination. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.29.591771. [PMID: 38746255 PMCID: PMC11092510 DOI: 10.1101/2024.04.29.591771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Spermatogenesis is a key developmental process underlying the origination of newly evolved genes. However, rapid cell type-specific transcriptomic divergence of the Drosophila germline has posed a significant technical barrier for comparative single-cell RNA-sequencing (scRNA-Seq) studies. By quantifying a surprisingly strong correlation between species-and cell type-specific divergence in three closely related Drosophila species, we apply a simple statistical procedure to identify a core set of 198 genes that are highly predictive of cell type identity while remaining robust to species-specific differences that span over 25-30 million years of evolution. We then utilize cell type classifications based on the 198-gene set to show how transcriptional divergence in cell type increases throughout spermatogenic developmental time, contrasting with traditional hourglass models of whole-organism development. With these cross-species cell type classifications, we then investigate the influence of genome organization on the molecular evolution of spermatogenesis vis-a-vis transcriptional bursting. We first demonstrate how mechanistic control of pre-meiotic transcription is achieved by altering transcriptional burst size while post-meiotic control is exerted via altered bursting frequency. We then report how global differences in autosomal vs. X chromosomal transcription likely arise in a developmental stage preceding full testis organogenesis by showing evolutionarily conserved decreases in X-linked transcription bursting kinetics in all examined somatic and germline cell types. Finally, we provide evidence supporting the cultivator model of de novo gene origination by demonstrating how the appearance of newly evolved testis-specific transcripts potentially provides short-range regulation of the transcriptional bursting properties of neighboring genes during key stages of spermatogenesis.
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13
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Frese AN, Mariossi A, Levine MS, Wühr M. Quantitative proteome dynamics across embryogenesis in a model chordate. iScience 2024; 27:109355. [PMID: 38510129 PMCID: PMC10951915 DOI: 10.1016/j.isci.2024.109355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/11/2023] [Accepted: 02/23/2024] [Indexed: 03/22/2024] Open
Abstract
The evolution of gene expression programs underlying the development of vertebrates remains poorly characterized. Here, we present a comprehensive proteome atlas of the model chordate Ciona, covering eight developmental stages and ∼7,000 translated genes, accompanied by a multi-omics analysis of co-evolution with the vertebrate Xenopus. Quantitative proteome comparisons argue against the widely held hourglass model, based solely on transcriptomic profiles, whereby peak conservation is observed during mid-developmental stages. Our analysis reveals maximal divergence at these stages, particularly gastrulation and neurulation. Together, our work provides a valuable resource for evaluating conservation and divergence of multi-omics profiles underlying the diversification of vertebrates.
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Affiliation(s)
- Alexander N. Frese
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Andrea Mariossi
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Michael S. Levine
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Martin Wühr
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
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14
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Giudice L, Mohamed A, Malm T. StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling. PLoS Comput Biol 2024; 20:e1012022. [PMID: 38607982 PMCID: PMC11042724 DOI: 10.1371/journal.pcbi.1012022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/24/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.
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Affiliation(s)
- Luca Giudice
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ahmed Mohamed
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tarja Malm
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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15
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Banerjee B, Das D. Effects of bursty synthesis in organelle biogenesis. Math Biosci 2024; 370:109156. [PMID: 38346665 DOI: 10.1016/j.mbs.2024.109156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024]
Abstract
A fundamental question of cell biology is how cells control the number of organelles. The processes of organelle biogenesis, namely de novo synthesis, fission, fusion, and decay, are inherently stochastic, producing cell-to-cell variability in organelle abundance. In addition, experiments suggest that the synthesis of some organelles can be bursty. We thus ask how bursty synthesis impacts intracellular organelle number distribution. We develop an organelle biogenesis model with bursty de novo synthesis by considering geometrically distributed burst sizes. We analytically solve the model in biologically relevant limits and provide exact expressions for the steady-state organelle number distributions and their means and variances. We also present approximate solutions for the whole model, complementing with exact stochastic simulations. We show that bursts generally increase the noise in organelle numbers, producing distinct signatures in noise profiles depending on different mechanisms of organelle biogenesis. We also find different shapes of organelle number distributions, including bimodal distributions in some parameter regimes. Notably, bursty synthesis broadens the parameter regime of observing bimodality compared to the 'non-bursty' case. Together, our framework utilizes number fluctuations to elucidate the role of bursty synthesis in producing organelle number heterogeneity in cells.
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Affiliation(s)
- Binayak Banerjee
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia 741 246, West Bengal, India
| | - Dipjyoti Das
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia 741 246, West Bengal, India.
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16
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Cheng L, Lv X, Zhou W, Li H, Yang Q, Chen X, Wu Y. A Microfluidic Chip for Single-Cell Capture Based on Stagnation Point Flow and Boundary Effects. MICROMACHINES 2024; 15:456. [PMID: 38675267 PMCID: PMC11051830 DOI: 10.3390/mi15040456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 03/21/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024]
Abstract
The capture of individual cells using microfluidic chips represents a widely adopted and efficient approach for investigating the biochemical microenvironment of singular cells. While conventional methods reliant on boundary effects pose challenges in precisely manipulating individual cells, single-cell capture grounded in the principle of stagnation point flow offers a solution to this limitation. Nevertheless, such capture mechanisms encounter inconsistency due to the instability of the flow field and stagnation point. In this study, a microfluidic device for the stable capture of single cells was designed, integrating the principle of fluid mechanics by amalgamating stagnation point flow and boundary effects. This innovative microfluidic chip transcended the limitations associated with single methodologies, leveraging the strengths of both stagnation point flow and boundary effects to achieve reliable single-cell capture. Notably, the incorporation of capture ports at the stagnation point not only harnessed boundary effects but also enhanced capture efficiency significantly, elevating it from 31.9% to 83.3%, thereby augmenting capture stability. Furthermore, computational simulations demonstrated the efficacy of the capture ports in entrapping particles of varying diameters, including 9 μm, 14 μm, and 18 μm. Experiment validation underscored the capability of this microfluidic system to capture single cells within the chip, maintaining stability even under flow rate perturbations spanning from 60 μL/min to 120 μL/min. Consequently, cells with dimensions between 8 μm and 12 μm can be reliably captured. The designed microfluidic system not only furnishes a straightforward and efficient experimental platform but also holds promise for facilitating deeper investigations into the intricate interplay between individual cells and their surrounding microenvironment.
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Affiliation(s)
- Long Cheng
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (L.C.); (X.L.); (Q.Y.); (X.C.); (Y.W.)
- University of Chinese Academy of Sciences, Beijing 101408, China
- State Key Laboratory of Applied Optics, Changchun 130033, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Xiao Lv
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (L.C.); (X.L.); (Q.Y.); (X.C.); (Y.W.)
- State Key Laboratory of Applied Optics, Changchun 130033, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Wenchao Zhou
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (L.C.); (X.L.); (Q.Y.); (X.C.); (Y.W.)
- State Key Laboratory of Applied Optics, Changchun 130033, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Huan Li
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (L.C.); (X.L.); (Q.Y.); (X.C.); (Y.W.)
- State Key Laboratory of Applied Optics, Changchun 130033, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Qiushuang Yang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (L.C.); (X.L.); (Q.Y.); (X.C.); (Y.W.)
- University of Chinese Academy of Sciences, Beijing 101408, China
- State Key Laboratory of Applied Optics, Changchun 130033, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Xing Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (L.C.); (X.L.); (Q.Y.); (X.C.); (Y.W.)
- University of Chinese Academy of Sciences, Beijing 101408, China
- State Key Laboratory of Applied Optics, Changchun 130033, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Yihui Wu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (L.C.); (X.L.); (Q.Y.); (X.C.); (Y.W.)
- State Key Laboratory of Applied Optics, Changchun 130033, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
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17
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Wu B, Holehouse J, Grima R, Jia C. Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition. J Chem Phys 2024; 160:074105. [PMID: 38364008 DOI: 10.1063/5.0188455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/19/2024] [Indexed: 02/18/2024] Open
Abstract
In this study, we obtain an exact time-dependent solution of the chemical master equation (CME) of an extension of the two-state telegraph model describing bursty or non-bursty protein expression in the presence of positive or negative autoregulation. Using the method of spectral decomposition, we show that the eigenfunctions of the generating function solution of the CME are Heun functions, while the eigenvalues can be determined by solving a continued fraction equation. Our solution generalizes and corrects a previous time-dependent solution for the CME of a gene circuit describing non-bursty protein expression in the presence of negative autoregulation [Ramos et al., Phys. Rev. E 83, 062902 (2011)]. In particular, we clarify that the eigenvalues are generally not real as previously claimed. We also investigate the relationship between different types of dynamic behavior and the type of feedback, the protein burst size, and the gene switching rate.
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Affiliation(s)
- Bingjie Wu
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, China
| | - James Holehouse
- The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, New Mexico 87501, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, China
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18
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Santra S, Nayak I, Paladhi A, Das D, Banerjee A. Estimates of differential toxin expression governing heterogeneous intracellular lifespans of Streptococcus pneumoniae. J Cell Sci 2024; 137:jcs260891. [PMID: 38411297 DOI: 10.1242/jcs.260891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/10/2024] [Indexed: 02/28/2024] Open
Abstract
Following invasion of the host cell, pore-forming toxins secreted by pathogens compromise vacuole integrity and expose the microbe to diverse intracellular defence mechanisms. However, the quantitative correlation between toxin expression levels and consequent pore dynamics, fostering the intracellular life of pathogens, remains largely unexplored. In this study, using Streptococcus pneumoniae and its secreted pore-forming toxin pneumolysin (Ply) as a model system, we explored various facets of host-pathogen interactions in the host cytosol. Using time-lapse fluorescence imaging, we monitored pore formation dynamics and lifespans of different pneumococcal subpopulations inside host cells. Based on experimental histograms of various event timescales such as pore formation time, vacuolar death or cytosolic escape time and total degradation time, we developed a mathematical model based on first-passage processes that could correlate the event timescales to intravacuolar toxin accumulation. This allowed us to estimate Ply production rate, burst size and threshold Ply quantities that trigger these outcomes. Collectively, we present a general method that illustrates a correlation between toxin expression levels and pore dynamics, dictating intracellular lifespans of pathogens.
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Affiliation(s)
- Shweta Santra
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India
| | - Indrani Nayak
- Department of Physics, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India
| | - Ankush Paladhi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India
| | - Dibyendu Das
- Department of Physics, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India
| | - Anirban Banerjee
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India
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19
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Grima R, Esmenjaud PM. Quantifying and correcting bias in transcriptional parameter inference from single-cell data. Biophys J 2024; 123:4-30. [PMID: 37885177 PMCID: PMC10808030 DOI: 10.1016/j.bpj.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/12/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.
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Affiliation(s)
- Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
| | - Pierre-Marie Esmenjaud
- Biology Department, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
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20
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Hong L, Wang Z, Zhang Z, Luo S, Zhou T, Zhang J. Phase separation reduces cell-to-cell variability of transcriptional bursting. Math Biosci 2024; 367:109127. [PMID: 38070763 DOI: 10.1016/j.mbs.2023.109127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 12/25/2023]
Abstract
Gene expression is a stochastic and noisy process often occurring in "bursts". Experiments have shown that the compartmentalization of proteins by liquid-liquid phase separation is conducive to reducing the noise of gene expression. Therefore, an important goal is to explore the role of bursts in phase separation noise reduction processes. We propose a coupled model that includes phase separation and a two-state gene expression process. Using the timescale separation method, we obtain approximate solutions for the expectation, variance, and noise strength of the dilute phase. We find that a higher burst frequency weakens the ability of noise reduction by phase separation, but as the burst size increases, this ability first increases and then decreases. This study provides a deeper understanding of phase separation to reduce noise in the stochastic gene expression with burst kinetics.
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Affiliation(s)
- Lijun Hong
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, PR China; School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, 510275, PR China
| | - Zihao Wang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, PR China; School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, 510275, PR China
| | - Zhenquan Zhang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, PR China; School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, 510275, PR China
| | - Songhao Luo
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, PR China; School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, 510275, PR China; Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, PR China; School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, 510275, PR China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, PR China; School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, 510275, PR China.
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21
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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22
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Nanda P, Barrere J, LaBar T, Murray AW. Multicellular growth as a dynamic network of cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.02.565242. [PMID: 37961646 PMCID: PMC10635083 DOI: 10.1101/2023.11.02.565242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Cell division without cell separation produces multicellular clusters in budding yeast. Two fundamental characteristics of these clusters are their size (the number of cells per cluster) and cellular composition: the fractions of cells with different phenotypes. However, we do not understand how different cellular features quantitatively influence these two phenotypes. Using cells as nodes and links between mother and daughter cells as edges, we model cluster growth and breakage by varying three parameters: the cell division rate, the rate at which intercellular connections break, and the kissing number (the maximum number of connections to one cell). We find that the kissing number sets the maximum possible cluster size. Below this limit, the ratio of the cell division rate to the connection breaking rate determines the cluster size. If links have a constant probability of breaking per unit time, the probability that a link survives decreases exponentially with its age. Modeling this behavior recapitulates experimental data. We then use this framework to examine synthetic, differentiating clusters with two cell types, faster-growing germ cells and their somatic derivatives. The fraction of clusters that contain both cell types increases as either of two parameters increase: the kissing number and difference between the growth rate of germ and somatic cells. In a population of clusters, the variation in cellular composition is inversely correlated (r2=0.87) with the average fraction of somatic cells in clusters. Our results show how a small number of cellular features can control the phenotypes of multicellular clusters that were potentially the ancestors of more complex forms of multicellular development, organization, and reproduction.
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Affiliation(s)
- Piyush Nanda
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Julien Barrere
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Thomas LaBar
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Andrew W. Murray
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
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23
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Wang X, Li Y, Jia C. Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks. J R Soc Interface 2023; 20:20230467. [PMID: 38016635 PMCID: PMC10684348 DOI: 10.1098/rsif.2023.0467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023] Open
Abstract
Stochastic gene expression dynamics can be modelled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiner's Poisson representation. Here, we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. This generalizes the results obtained in Dattani & Barahona (Dattani & Barahona 2017 J. R. Soc. Interface 14, 20160833 (doi:10.1098/rsif.2016.0833)). In addition, using a simple counter-example, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.
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Affiliation(s)
- Xinyu Wang
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, People’s Republic of China
| | - Youming Li
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
| | - Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, People’s Republic of China
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24
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Xie XS. Round-Trip Journey of a Physical Chemist. J Phys Chem B 2023; 127:7800-7809. [PMID: 37731371 DOI: 10.1021/acs.jpcb.3c05597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Affiliation(s)
- Xiaoliang Sunney Xie
- Biomedical Pioneering Innovation Center, Peking University, 5 Yiheyuan Road, Beijing 100871, China
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25
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Douaihy M, Topno R, Lagha M, Bertrand E, Radulescu O. BurstDECONV: a signal deconvolution method to uncover mechanisms of transcriptional bursting in live cells. Nucleic Acids Res 2023; 51:e88. [PMID: 37522372 PMCID: PMC10484743 DOI: 10.1093/nar/gkad629] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 06/23/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023] Open
Abstract
Monitoring transcription in living cells gives access to the dynamics of this complex fundamental process. It reveals that transcription is discontinuous, whereby active periods (bursts) are separated by one or several types of inactive periods of distinct lifetimes. However, decoding temporal fluctuations arising from live imaging and inferring the distinct transcriptional steps eliciting them is a challenge. We present BurstDECONV, a novel statistical inference method that deconvolves signal traces into individual transcription initiation events. We use the distribution of waiting times between successive polymerase initiation events to identify mechanistic features of transcription such as the number of rate-limiting steps and their kinetics. Comparison of our method to alternative methods emphasizes its advantages in terms of precision and flexibility. Unique features such as the direct determination of the number of promoter states and the simultaneous analysis of several potential transcription models make BurstDECONV an ideal analytic framework for live cell transcription imaging experiments. Using simulated realistic data, we found that our method is robust with regards to noise or suboptimal experimental designs. To show its generality, we applied it to different biological contexts such as Drosophila embryos or human cells.
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Affiliation(s)
- Maria Douaihy
- LPHI, University of Montpellier and CNRS, Place Eugène Bataillon, Montpellier 34095, France
- IGMM, University of Montpellier and CNRS, 1919 Rte de Mende, Montpellier 34090, France
| | - Rachel Topno
- LPHI, University of Montpellier and CNRS, Place Eugène Bataillon, Montpellier 34095, France
- IGH, University of Montpellier and CNRS, 141 Rue de la Cardonille, Montpellier 34094, France
| | - Mounia Lagha
- IGMM, University of Montpellier and CNRS, 1919 Rte de Mende, Montpellier 34090, France
| | - Edouard Bertrand
- IGH, University of Montpellier and CNRS, 141 Rue de la Cardonille, Montpellier 34094, France
| | - Ovidiu Radulescu
- LPHI, University of Montpellier and CNRS, Place Eugène Bataillon, Montpellier 34095, France
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26
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Li S, Coffinier Y, Lagadec C, Cleri F, Nishiguchi K, Fujiwara A, Kim SH, Clément N. Single-Cell Electrochemical Aptasensor Array. ACS Sens 2023; 8:2921-2926. [PMID: 37431846 DOI: 10.1021/acssensors.3c00570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Despite several demonstrations of electrochemical devices with limits of detection (LOD) of 1 cell/mL, the implementation of single-cell bioelectrochemical sensor arrays has remained elusive due to the challenges of scaling up. In this study, we show that the recently introduced nanopillar array technology combined with redox-labeled aptamers targeting epithelial cell adhesion molecule (EpCAM) is perfectly suited for such implementation. Combining nanopillar arrays with microwells determined for single cell trapping directly on the sensor surface, single target cells are successfully detected and analyzed. This first implementation of a single-cell electrochemical aptasensor array, based on Brownian-fluctuating redox species, opens new opportunities for large-scale implementation and statistical analysis of early cancer diagnosis and cancer therapy in clinical settings.
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Affiliation(s)
- Shuo Li
- IIS, LIMMS/CNRS-IIS IRL2820, The Univ. of Tokyo 4-6-1 Komaba, Meguro-ku 153-8505, Tokyo, Japan
| | - Yannick Coffinier
- IEMN, CNRS UMR8520, Univ. Lille Avenue Poincare, BP 60069, Villeneuve d'Ascq cedex 59652, France
| | - Chann Lagadec
- Univ. Lille, CNRS, Inserm, CHU Lille, Centre Oscar Lambret, UMR9020 - UMR-S 1277 - Canther - Cancer Heterogeneity, Plasticity and Resistance to Therapies, Lille F-59000, France
| | - Fabrizio Cleri
- IEMN, CNRS UMR8520, Univ. Lille Avenue Poincare, BP 60069, Villeneuve d'Ascq cedex 59652, France
| | - Katsuhiko Nishiguchi
- NTT Basic Research Laboratories, NTT Corporation, 3-1, Morinosato-Wakamiya, Atsugi-shi 243-0198, Japan
| | - Akira Fujiwara
- NTT Basic Research Laboratories, NTT Corporation, 3-1, Morinosato-Wakamiya, Atsugi-shi 243-0198, Japan
| | - Soo Hyeon Kim
- IIS, LIMMS/CNRS-IIS IRL2820, The Univ. of Tokyo 4-6-1 Komaba, Meguro-ku 153-8505, Tokyo, Japan
| | - Nicolas Clément
- IIS, LIMMS/CNRS-IIS IRL2820, The Univ. of Tokyo 4-6-1 Komaba, Meguro-ku 153-8505, Tokyo, Japan
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27
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Klumpe HE, Lugagne JB, Khalil AS, Dunlop MJ. Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes. ACS Synth Biol 2023; 12:2367-2381. [PMID: 37467372 DOI: 10.1021/acssynbio.3c00203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.
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Affiliation(s)
- Heidi E Klumpe
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
| | - Jean-Baptiste Lugagne
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
| | - Ahmad S Khalil
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, United States
| | - Mary J Dunlop
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
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28
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Livingston NM, Kwon J, Valera O, Saba JA, Sinha NK, Reddy P, Nelson B, Wolfe C, Ha T, Green R, Liu J, Wu B. Bursting translation on single mRNAs in live cells. Mol Cell 2023; 83:2276-2289.e11. [PMID: 37329884 PMCID: PMC10330622 DOI: 10.1016/j.molcel.2023.05.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 03/27/2023] [Accepted: 05/14/2023] [Indexed: 06/19/2023]
Abstract
Stochasticity has emerged as a mechanism of gene regulation. Much of this so-called "noise" has been attributed to bursting transcription. Although bursting transcription has been studied extensively, the role of stochasticity in translation has not been fully investigated due to the lack of enabling imaging technology. In this study, we developed techniques to track single mRNAs and their translation in live cells for hours, allowing the measurement of previously uncharacterized translation dynamics. We applied genetic and pharmacological perturbations to control translation kinetics and found that, like transcription, translation is not a constitutive process but instead cycles between inactive and active states, or "bursts." However, unlike transcription, which is largely frequency-modulated, complex structures in the 5'-untranslated region alter burst amplitudes. Bursting frequency can be controlled through cap-proximal sequences and trans-acting factors such as eIF4F. We coupled single-molecule imaging with stochastic modeling to quantitatively determine the kinetic parameters of translational bursting.
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Affiliation(s)
- Nathan M Livingston
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jiwoong Kwon
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Oliver Valera
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - James A Saba
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Niladri K Sinha
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Pranav Reddy
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Blake Nelson
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Clara Wolfe
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Taekjip Ha
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Rachel Green
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Jian Liu
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Bin Wu
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Center for Cell Dynamics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Solomon H Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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29
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Wu Y, Fu Y, Guo J, Guo J. Single-molecule immunoassay technology: Recent advances. Talanta 2023; 265:124903. [PMID: 37418954 DOI: 10.1016/j.talanta.2023.124903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/29/2023] [Accepted: 07/01/2023] [Indexed: 07/09/2023]
Abstract
Detecting diseases at the molecular level aids in early diagnosis and treatment. However, traditional immunological detection techniques, such as enzyme-linked immunosorbent assay (ELISA) and chemiluminescence, have detection sensitivities between 10-16 and 10-12 mol/L, which are inadequate for early diagnosis. Single-molecule immunoassays can reach detection sensitivities of 10-18 mol/L and can detect biomarkers that are difficult to measure using conventional detection techniques. It can confine molecules to be detected in a small spatial area and provide absolute counting of the detected signal, offering the advantage of high efficiency and accuracy. Herein, we demonstrate the principles and equipment of two single-molecule immunoassay techniques and discuss their applications. It is shown that the detection sensitivity can be improved by 2-3 orders of magnitude compared to common chemiluminescence or ELISA assays. The microarray-based single-molecule immunoassay technique can test 66 samples in 1 h, which is more efficient than conventional immunological detection techniques. In contrast, microdroplet-based single-molecule immunoassay techniques can generate 107 droplets in 10 min, which is more than 100 times faster than a single droplet generator. By comparing the two single-molecule immunoassay techniques, we highlight our personal perspectives on the current limitations of point-of-care applications and future development trends.
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Affiliation(s)
- Yi Wu
- University of Electronic Science and Technology of China, Chengdu, China
| | - Yusheng Fu
- University of Electronic Science and Technology of China, Chengdu, China
| | - Jiuchuan Guo
- University of Electronic Science and Technology of China, Chengdu, China.
| | - Jinhong Guo
- School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China; The M.O.E. Key Laboratory of Laboratory Medical Diagnostics, The College of Laboratory Medicine, Chongqing Medical University, #1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
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30
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Antunes AS, Martins-de-Souza D. Single-Cell RNA Sequencing and Its Applications in the Study of Psychiatric Disorders. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:329-339. [PMID: 37519459 PMCID: PMC10382703 DOI: 10.1016/j.bpsgos.2022.03.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 10/18/2022] Open
Abstract
Neuroscience is currently one of the most challenging research fields owing to the enormous complexity of the mammalian nervous system. We are yet to understand precise transcriptional programs that govern cell fate during neurodevelopment, resolve the connectome of the mammalian brain, and determine the etiology of various neurodegenerative and psychiatric disorders. Technological advances in the past decade, notably single-cell RNA sequencing, have enabled huge progress in our understanding of such features. Our current knowledge of the transcriptome is largely derived from bulk RNA sequencing, which reveals only the average gene expression of millions of cells, potentially missing out on minor transcriptome differences between cells detectable only at single-cell resolution. Since 2009, several single-cell RNA sequencing techniques have emerged that enable the accurate classification of neuronal and glial cell subtypes beyond classical molecular markers and electrophysiological features and allow the identification of previously unknown cell types. Furthermore, it enables the interrogation of molecular and disease-relevant mechanisms and offers further possibilities for the discovery of new drug targets and disease biomarkers. This review intends to familiarize the reader with the main single-cell RNA sequencing techniques developed throughout the past decade and discusses their application in the fields of brain cell taxonomy, neurodevelopment, and psychiatric disorders.
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Affiliation(s)
- André S.L.M. Antunes
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, State University of Campinas, Campinas, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, State University of Campinas, Campinas, Brazil
- Experimental Medicine Research Cluster, University of Campinas, Campinas, Brazil
- D'Or Institute for Research and Education, Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria, Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo, Brazil
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31
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Gerling T, Godino N, Pfisterer F, Hupf N, Kirschbaum M. High-precision, low-complexity, high-resolution microscopy-based cell sorting. LAB ON A CHIP 2023. [PMID: 37314345 DOI: 10.1039/d3lc00242j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous flow cell sorting based on image analysis is a powerful concept that exploits spatially-resolved features in cells, such as subcellular protein localisation or cell and organelle morphology, to isolate highly specialised cell types that were previously inaccessible to biomedical research, biotechnology, and medicine. Recently, sorting protocols have been proposed that achieve impressive throughput by combining ultra-high flow rates with sophisticated imaging and data processing protocols. However, moderate image quality and high complex experimental setups still prevent the full potential of image-activated cell sorting from being a general-purpose tool. Here, we present a new low-complexity microfluidic approach based on high numerical aperture wide-field microscopy and precise dielectrophoretic cell handling. It provides high-quality images with unprecedented resolution in image-activated cell sorting (i.e., 216 nm). In addition, it also allows long image processing times of several hundred milliseconds for thorough image analysis, while ensuring reliable and low-loss cell processing. Using our approach, we sorted live T cells based on subcellular localisation of fluorescence signals and demonstrated that purities above 80% are possible while targeting maximum yields and sample volume throughputs in the range of μl min-1. We were able to recover 85% of the target cells analysed. Finally, we ensure and quantify the full vitality of the sorted cells cultivating the cells for a period of time and through colorimetric viability tests.
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Affiliation(s)
- Tobias Gerling
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
- Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany
| | - Neus Godino
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Felix Pfisterer
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Nina Hupf
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Michael Kirschbaum
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
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32
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Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
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Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
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33
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Aquino G, Rocco A. Noise-induced transitions in gene circuits: A perturbative approach for slow noise. Phys Rev E 2023; 107:044401. [PMID: 37198804 DOI: 10.1103/physreve.107.044401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/21/2023] [Indexed: 05/19/2023]
Abstract
We consider a generic class of gene circuits affected by nonlinear extrinsic noise. To address this nonlinearity we introduce a general perturbative methodology based on assuming timescale separation between noise and gene dynamics, with fluctuations exhibiting a large but finite correlation time. We apply this methodology to the case of the toggle switch, and by considering biologically relevant log-normal fluctuations, we find that the system exhibits noise-induced transitions. The system becomes bimodal in regions of the parameter space where it would be deterministically monostable. We show that by including higher order corrections our methodology allows one to obtain correct predictions for the occurrence of transitions even for not so large correlation time of the fluctuations, thereby overcoming limitations of previous theoretical approaches. Interestingly we find that at intermediate noise intensities the noise-induced transition in the toggle switch affects one of the genes involved, but not the other one.
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Affiliation(s)
- Gerardo Aquino
- Department of Computing, Goldsmiths, University of London, SE14 6AD London, United Kingdom
| | - Andrea Rocco
- Department of Physics, University of Surrey, GU2 7XH Guildford, United Kingdom
- Department of Microbial Sciences, University of Surrey, GU2 7XH, Guildford, United Kingdom
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34
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Keyl P, Bischoff P, Dernbach G, Bockmayr M, Fritz R, Horst D, Blüthgen N, Montavon G, Müller KR, Klauschen F. Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Res 2023; 51:e20. [PMID: 36629274 PMCID: PMC9976884 DOI: 10.1093/nar/gkac1212] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/16/2022] [Accepted: 12/06/2022] [Indexed: 01/12/2023] Open
Abstract
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.
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Affiliation(s)
- Philipp Keyl
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Philip Bischoff
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Gabriel Dernbach
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Pediatric Hematology and Oncolog, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf Martinistr. 52, 20246 Hamburg, Germany
| | - Rebecca Fritz
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Institut für Biologie, Humboldt University, Free University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
| | - Grégoire Montavon
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
| | - Klaus-Robert Müller
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
- Department of Artificial Intelligence, Korea University, Seoul 136-713, South Korea
- Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 4, 66123 Saarbrücken, Germany
| | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute of Pathology, Ludwig-Maximilians-University Munich, Thalkirchner Str. 36, 80337 München, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich partner site, Germany
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35
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Biochemical analysis based on optical detection integrated microfluidic chip. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2022.116865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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36
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Molecular Origins of Transcriptional Heterogeneity in Diazotrophic Klebsiella oxytoca. mSystems 2022; 7:e0059622. [PMID: 36073804 PMCID: PMC9600154 DOI: 10.1128/msystems.00596-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Phenotypic heterogeneity in clonal bacterial batch cultures has been shown for a range of bacterial systems; however, the molecular origins of such heterogeneity and its magnitude are not well understood. Under conditions of extreme low-nitrogen stress in the model diazotroph Klebsiella oxytoca, we found remarkably high heterogeneity of nifHDK gene expression, which codes for the structural genes of nitrogenase, one key enzyme of the global nitrogen cycle. This heterogeneity limited the bulk observed nitrogen-fixing capacity of the population. Using dual-probe, single-cell RNA fluorescent in situ hybridization, we correlated nifHDK expression with that of nifLA and glnK-amtB, which code for the main upstream regulatory components. Through stochastic transcription models and mutual information analysis, we revealed likely molecular origins for heterogeneity in nitrogenase expression. In the wild type and regulatory variants, we found that nifHDK transcription was inherently bursty, but we established that noise propagation through signaling was also significant. The regulatory gene glnK had the highest discernible effect on nifHDK variance, while noise from factors outside the regulatory pathway were negligible. Understanding the basis of inherent heterogeneity of nitrogenase expression and its origins can inform biotechnology strategies seeking to enhance biological nitrogen fixation. Finally, we speculate on potential benefits of diazotrophic heterogeneity in natural soil environments. IMPORTANCE Nitrogen is an essential micronutrient for both plant and animal life and naturally exists in both reactive and inert chemical forms. Modern agriculture is heavily reliant on nitrogen that has been "fixed" into a reactive form via the energetically expensive Haber-Bosch process, with significant environmental consequences. Nitrogen-fixing bacteria provide an alternative source of fixed nitrogen for use in both biotechnological and agricultural settings, but this relies on a firm understanding of how the fixation process is regulated within individual bacterial cells. We examined the cell-to-cell variability in the nitrogen-fixing behavior of Klebsiella oxytoca, a free-living bacterium. The significance of our research is in identifying not only the presence of marked variability but also the specific mechanisms that give rise to it. This understanding gives insight into both the evolutionary advantages of variable behavior as well as strategies for biotechnological applications.
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Koraichi MB, Touzel MP, Mazzolini A, Mora T, Walczak AM. NoisET: Noise Learning and Expansion Detection of T-Cell Receptors. J Phys Chem A 2022; 126:7407-7414. [PMID: 36178325 DOI: 10.1021/acs.jpca.2c05002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and data sets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.
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Affiliation(s)
- Meriem Bensouda Koraichi
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | | | - Andrea Mazzolini
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | - Thierry Mora
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | - Aleksandra M Walczak
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
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38
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Sukys A, Öcal K, Grima R. Approximating solutions of the Chemical Master equation using neural networks. iScience 2022; 25:105010. [PMID: 36117994 PMCID: PMC9474291 DOI: 10.1016/j.isci.2022.105010] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/13/2022] [Accepted: 08/18/2022] [Indexed: 10/27/2022] Open
Abstract
The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest. Although Monte Carlo methods provide a principled means to probe system dynamics, the large number of simulations typically required can render the estimation of molecule number distributions and other quantities infeasible. In this article, we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for the Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training neural networks to learn the distributions predicted by the CME from relatively few stochastic simulations. We show on biologically relevant examples that simple neural networks with one hidden layer can capture highly complex distributions across parameter space, thereby accelerating computationally intensive tasks such as parameter exploration and inference.
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Affiliation(s)
- Augustinas Sukys
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK
- The Alan Turing Institute, London NW1 2DB, UK
| | - Kaan Öcal
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK
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39
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Abstract
DNA looping has emerged as a central paradigm of transcriptional regulation, as it is shared across many living systems. One core property of DNA looping-based regulation is its ability to greatly enhance repression or activation of genes with only a few copies of transcriptional regulators. However, this property based on a small number of proteins raises the question of the robustness of such a mechanism with respect to the large intracellular perturbations taking place during growth and division of the cell. Here we address the issue of sensitivity to variations of intracellular parameters of gene regulation by DNA looping. We use the lac system as a prototype to experimentally identify the key features of the robustness of DNA looping in growing Escherichia coli cells. Surprisingly, we observe time intervals of tight repression spanning across division events, which can sometimes exceed 10 generations. Remarkably, the distribution of such long time intervals exhibits memoryless statistics that is mostly insensitive to repressor concentration, cell division events, and the number of distinct loops accessible to the system. By contrast, gene regulation becomes highly sensitive to these perturbations when DNA looping is absent. Using stochastic simulations, we propose that the observed robustness to division emerges from the competition between fast, multiple rebinding events of repressors and slow initiation rate of the RNA polymerase. We argue that fast rebinding events are a direct consequence of DNA looping that ensures robust gene repression across a range of intracellular perturbations.
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40
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Öcal K, Gutmann MU, Sanguinetti G, Grima R. Inference and uncertainty quantification of stochastic gene expression via synthetic models. J R Soc Interface 2022; 19:20220153. [PMID: 35858045 PMCID: PMC9277240 DOI: 10.1098/rsif.2022.0153] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/21/2022] [Indexed: 12/26/2022] Open
Abstract
Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.
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Affiliation(s)
- Kaan Öcal
- School of Informatics, University of Edinburgh, Edinburgh EH9 3JH, UK
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK
| | | | - Guido Sanguinetti
- Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK
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41
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Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks. PLoS Comput Biol 2022; 18:e1010183. [PMID: 35731728 PMCID: PMC9216546 DOI: 10.1371/journal.pcbi.1010183] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 05/07/2022] [Indexed: 11/19/2022] Open
Abstract
Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.
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42
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Frequency modulation of a bacterial quorum sensing response. Nat Commun 2022; 13:2772. [PMID: 35589697 PMCID: PMC9120067 DOI: 10.1038/s41467-022-30307-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 04/21/2022] [Indexed: 11/09/2022] Open
Abstract
In quorum sensing, bacteria secrete or release small molecules into the environment that, once they reach a certain threshold, trigger a behavioural change in the population. As the concentration of these so-called autoinducers is supposed to reflect population density, they were originally assumed to be continuously produced by all cells in a population. However, here we show that in the α-proteobacterium Sinorhizobium meliloti expression of the autoinducer synthase gene is realized in asynchronous stochastic pulses that result from scarcity and, presumably, low binding affinity of the key activator. Physiological cues modulate pulse frequency, and pulse frequency in turn modulates the velocity with which autoinducer levels in the environment reach the threshold to trigger the quorum sensing response. We therefore propose that frequency-modulated pulsing in S. meliloti represents the molecular mechanism for a collective decision-making process in which each cell's physiological state and need for behavioural adaptation is encoded in the pulse frequency with which it expresses the autoinducer synthase gene; the pulse frequencies of all members of the population are then integrated in the common pool of autoinducers, and only once this vote crosses the threshold, the response behaviour is initiated.
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43
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Wang Y, Zhao J, Park HJ, Zhou D. Effect of dedifferentiation on noise propagation in cellular hierarchy. Phys Rev E 2022; 105:054409. [PMID: 35706189 DOI: 10.1103/physreve.105.054409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
Many fast renewing tissues have a hierarchical structure. Tissue-specific stem cells are at the root of this cellular hierarchy, which give rive to a whole range of specialized cells via cellular differentiation. However, increasing evidence shows that the hierarchical structure can be broken due to cellular dedifferentiation in which cells at differentiated stages can revert to the stem cell stage. Dedifferentiation has significant impacts on many aspects of hierarchical tissues. Here we investigate the effect of dedifferentiation on noise propagation by developing a stochastic model composed of different cell types. The moment equations are derived, via which we systematically investigate how the noise in the cell number is changed by dedifferentiation. Our results suggest that dedifferentiation have different effects on the noises in the numbers of stem cells and nonstem cells. Specifically, the noise in the number of stem cells is significantly reduced by increasing dedifferentiation probability. Due to the dual effect of dedifferentiation on nonstem cells, however, more complex changes could happen to the noise in the number of nonstem cells by increasing dedifferentiation probability. Furthermore, it is found that even though dedifferentiation could turn part of the noise propagation process into a noise-amplifying step, it is very unlikely to turn the entire process into a noise-amplifying cascade.
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Affiliation(s)
- Yuman Wang
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China
| | - Jintong Zhao
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China
| | - Hye Jin Park
- Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea
- Department of Physics, Inha University, Incheon 22212, Republic of Korea
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China
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44
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Hansberg W. A critical analysis on the conception of "Pre-existent gene expression programs" for cell differentiation and development. Differentiation 2022; 125:1-8. [DOI: 10.1016/j.diff.2022.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 11/15/2022]
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45
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Xie H, Ding X. The Intriguing Landscape of Single-Cell Protein Analysis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105932. [PMID: 35199955 PMCID: PMC9036017 DOI: 10.1002/advs.202105932] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/27/2022] [Indexed: 05/15/2023]
Abstract
Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.
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Affiliation(s)
- Haiyang Xie
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Xianting Ding
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
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46
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Lawing AM, McCoy M, Reinke BA, Sarkar SK, Smith FA, Wright D. A Framework for Investigating Rules of Life by Establishing Zones of Influence. Integr Comp Biol 2022; 61:2095-2108. [PMID: 34297089 PMCID: PMC8825771 DOI: 10.1093/icb/icab169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/26/2021] [Accepted: 07/20/2021] [Indexed: 12/18/2022] Open
Abstract
The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (a) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (b) island biogeography, and (c) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.
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Affiliation(s)
- A Michelle Lawing
- Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, 77843, USA
| | - Michael McCoy
- Department of Biology, East Carolina University, Greenville, NC 27858, USA
| | - Beth A Reinke
- Department of Biology, Northeastern Illinois University, IL 60625, USA
| | | | - Felisa A Smith
- Department of Biology, University of New Mexico, NM 87131, USA
| | - Derek Wright
- Department of Physics, Colorado School of Mines, CO 80401, USA
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47
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Rijal K, Prasad A, Singh A, Das D. Exact Distribution of Threshold Crossing Times for Protein Concentrations: Implication for Biological Timekeeping. PHYSICAL REVIEW LETTERS 2022; 128:048101. [PMID: 35148123 DOI: 10.1103/physrevlett.128.048101] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
Stochastic protein accumulation up to some concentration threshold sets the timing of many cellular physiological processes. Here we obtain the exact distribution of first threshold crossing times of protein concentration, in either Laplace or time domain, and its associated cumulants: mean, variance, and skewness. The distribution is asymmetric, and its skewness nonmonotonically varies with the threshold. We study lysis times of E. coli cells for holin gene mutants of bacteriophage-λ and find a good match with theory. Mutants requiring higher holin thresholds show more skewed lysis time distributions as predicted. The theory also predicts a linear relationship between infection delay time and host doubling time for lytic viruses, that has recently been experimentally observed.
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Affiliation(s)
- Krishna Rijal
- Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Ashok Prasad
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado 80523, USA
| | - Abhyudai Singh
- Departments of Electrical and Computer Engineering, Biomedical Engineering and Mathematical Sciences, University of Delaware, Newark, Delaware 19716, USA
| | - Dibyendu Das
- Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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48
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Ha T, Kaiser C, Myong S, Wu B, Xiao J. Next generation single-molecule techniques: Imaging, labeling, and manipulation in vitro and in cellulo. Mol Cell 2022; 82:304-314. [PMID: 35063098 DOI: 10.1016/j.molcel.2021.12.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 12/14/2021] [Accepted: 12/14/2021] [Indexed: 12/24/2022]
Abstract
Owing to their unique abilities to manipulate, label, and image individual molecules in vitro and in cellulo, single-molecule techniques provide previously unattainable access to elementary biological processes. In imaging, single-molecule fluorescence resonance energy transfer (smFRET) and protein-induced fluorescence enhancement in vitro can report on conformational changes and molecular interactions, single-molecule pull-down (SiMPull) can capture and analyze the composition and function of native protein complexes, and single-molecule tracking (SMT) in live cells reveals cellular structures and dynamics. In labeling, the abilities to specifically label genomic loci, mRNA, and nascent polypeptides in cells have uncovered chromosome organization and dynamics, transcription and translation dynamics, and gene expression regulation. In manipulation, optical tweezers, integration of single-molecule fluorescence with force measurements, and single-molecule force probes in live cells have transformed our mechanistic understanding of diverse biological processes, ranging from protein folding, nucleic acids-protein interactions to cell surface receptor function.
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Affiliation(s)
- Taekjip Ha
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Howard Hughes Medical Institute, Baltimore, MD 21205, USA.
| | - Christian Kaiser
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sua Myong
- Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Bin Wu
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Jie Xiao
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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49
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Urbaniec J, Xu Y, Hu Y, Hingley-Wilson S, McFadden J. Phenotypic heterogeneity in persisters: a novel 'hunker' theory of persistence. FEMS Microbiol Rev 2022; 46:fuab042. [PMID: 34355746 PMCID: PMC8767447 DOI: 10.1093/femsre/fuab042] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 08/04/2021] [Indexed: 12/11/2022] Open
Abstract
Persistence has been linked to treatment failure since its discovery over 70 years ago and understanding formation, nature and survival of this key antibiotic refractory subpopulation is crucial to enhancing treatment success and combatting the threat of antimicrobial resistance (AMR). The term 'persistence' is often used interchangeably with other terms such as tolerance or dormancy. In this review we focus on 'antibiotic persistence' which we broadly define as a feature of a subpopulation of bacterial cells that possesses the non-heritable character of surviving exposure to one or more antibiotics; and persisters as cells that possess this characteristic. We discuss novel molecular mechanisms involved in persister cell formation, as well as environmental factors which can contribute to increased antibiotic persistence in vivo, highlighting recent developments advanced by single-cell studies. We also aim to provide a comprehensive model of persistence, the 'hunker' theory which is grounded in intrinsic heterogeneity of bacterial populations and a myriad of 'hunkering down' mechanisms which can contribute to antibiotic survival of the persister subpopulation. Finally, we discuss antibiotic persistence as a 'stepping-stone' to AMR and stress the urgent need to develop effective anti-persister treatment regimes to treat this highly clinically relevant bacterial sub-population.
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Affiliation(s)
- J Urbaniec
- Department of Microbial Sciences and University of Surrey, Guildford, Surrey, GU27XH, UK
| | - Ye Xu
- Department of Microbial Sciences and University of Surrey, Guildford, Surrey, GU27XH, UK
| | - Y Hu
- Farnborough Sensonic limited, Farnborough road, GU14 7NA, UK
| | - S Hingley-Wilson
- Department of Microbial Sciences and University of Surrey, Guildford, Surrey, GU27XH, UK
| | - J McFadden
- Department of Microbial Sciences and University of Surrey, Guildford, Surrey, GU27XH, UK
- Quantum biology doctoral training centre, University of Surrey, Guildford, Surrey, GU27XH, UK
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50
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Chen M, Luo S, Cao M, Guo C, Zhou T, Zhang J. Exact distributions for stochastic gene expression models with arbitrary promoter architecture and translational bursting. Phys Rev E 2022; 105:014405. [PMID: 35193181 DOI: 10.1103/physreve.105.014405] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/14/2021] [Indexed: 11/07/2022]
Abstract
Gene expression in individual cells is inherently variable and sporadic, leading to cell-to-cell variability in mRNA and protein levels. Recent single-cell and single-molecule experiments indicate that promoter architecture and translational bursting play significant roles in controlling gene expression noise and generating the phenotypic diversity that life exhibits. To quantitatively understand the impact of these factors, it is essential to construct an accurate mathematical description of stochastic gene expression and find the exact analytical results, which is a formidable task. Here, we develop a stochastic model of bursty gene expression, which considers the complex promoter architecture governing the variability in mRNA expression and a general distribution characterizing translational burst. We derive the analytical expression for the corresponding protein steady-state distribution and all moment statistics of protein counts. We show that the total protein noise can be decomposed into three parts: the low-copy noise of protein due to probabilistic individual birth and death events, the noise due to stochastic switching between promoter states, and the noise resulting from translational busting. The theoretical results derived provide quantitative insights into the biochemical mechanisms of stochastic gene expression.
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Affiliation(s)
- Meiling Chen
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Songhao Luo
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Mengfang Cao
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Chengjun Guo
- School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510275, People's Republic of China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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