1
|
Singh A, Sen S, Iter M, Adelaja A, Luecke S, Guo X, Hoffmann A. Stimulus-response signaling dynamics characterize macrophage polarization states. Cell Syst 2024; 15:563-577.e6. [PMID: 38843840 PMCID: PMC11226196 DOI: 10.1016/j.cels.2024.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 12/03/2023] [Accepted: 05/10/2024] [Indexed: 06/22/2024]
Abstract
The functional state of cells is dependent on their microenvironmental context. Prior studies described how polarizing cytokines alter macrophage transcriptomes and epigenomes. Here, we characterized the functional responses of 6 differentially polarized macrophage populations by measuring the dynamics of transcription factor nuclear factor κB (NF-κB) in response to 8 stimuli. The resulting dataset of single-cell NF-κB trajectories was analyzed by three approaches: (1) machine learning on time-series data revealed losses of stimulus distinguishability with polarization, reflecting canalized effector functions. (2) Informative trajectory features driving stimulus distinguishability ("signaling codons") were identified and used for mapping a cell state landscape that could then locate macrophages conditioned by an unrelated condition. (3) Kinetic parameters, inferred using a mechanistic NF-κB network model, provided an alternative mapping of cell states and correctly predicted biochemical findings. Together, this work demonstrates that a single analyte's dynamic trajectories may distinguish the functional states of single cells and molecular network states underlying them. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- Apeksha Singh
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Supriya Sen
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Iter
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Adewunmi Adelaja
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Stefanie Luecke
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xiaolu Guo
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Alexander Hoffmann
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| |
Collapse
|
2
|
Kumar N, Mim MS, Dowling A, Zartman JJ. Reverse engineering morphogenesis through Bayesian optimization of physics-based models. NPJ Syst Biol Appl 2024; 10:49. [PMID: 38714708 PMCID: PMC11076624 DOI: 10.1038/s41540-024-00375-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: 08/21/2023] [Accepted: 04/17/2024] [Indexed: 05/10/2024] Open
Abstract
Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.
Collapse
Affiliation(s)
- Nilay Kumar
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Mayesha Sahir Mim
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Alexander Dowling
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Jeremiah J Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN, 46556, USA.
| |
Collapse
|
3
|
Kinnunen PC, Humphries BA, Luker GD, Luker KE, Linderman JJ. Characterizing heterogeneous single-cell dose responses computationally and experimentally using threshold inhibition surfaces and dose-titration assays. NPJ Syst Biol Appl 2024; 10:42. [PMID: 38637530 PMCID: PMC11026493 DOI: 10.1038/s41540-024-00369-x] [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: 12/16/2023] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately leading to treatment failures. While tumor heterogeneity is recognized as a major obstacle to cancer therapy, standard dose-response measurements for the potency of targeted kinase inhibitors aggregate populations of cells, obscuring intercellular variations in responses. In this work, we develop an analytical and experimental framework to quantify and model dose responses of individual cancer cells to drugs. We first explore the connection between population and single-cell dose responses using a computational model, revealing that multiple heterogeneous populations can yield nearly identical population dose responses. We demonstrate that a single-cell analysis method, which we term a threshold inhibition surface, can differentiate among these populations. To demonstrate the applicability of this method, we develop a dose-titration assay to measure dose responses in single cells. We apply this assay to breast cancer cells responding to phosphatidylinositol-3-kinase inhibition (PI3Ki), using clinically relevant PI3Kis on breast cancer cell lines expressing fluorescent biosensors for kinase activity. We demonstrate that MCF-7 breast cancer cells exhibit heterogeneous dose responses with some cells requiring over ten-fold higher concentrations than the population average to achieve inhibition. Our work reimagines dose-response relationships for cancer drugs in an emerging paradigm of single-cell tumor heterogeneity.
Collapse
Affiliation(s)
- Patrick C Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Brock A Humphries
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gary D Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kathryn E Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
| |
Collapse
|
4
|
Szigeti K, Ihnatovych I, Notari E, Dorn RP, Maly I, He M, Birkaya B, Prasad S, Byrne RS, Indurthi DC, Nimmer E, Heo Y, Retfalvi K, Chaves L, Sule N, Hofmann WA, Auerbach A, Wilding G, Bae Y, Reynolds J. CHRFAM7A diversifies human immune adaption through Ca 2+ signalling and actin cytoskeleton reorganization. EBioMedicine 2024; 103:105093. [PMID: 38569318 PMCID: PMC10999709 DOI: 10.1016/j.ebiom.2024.105093] [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: 10/10/2023] [Revised: 03/15/2024] [Accepted: 03/17/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Human restricted genes contribute to human specific traits in the immune system. CHRFAM7A, a uniquely human fusion gene, is a negative regulator of the α7 nicotinic acetylcholine receptor (α7 nAChR), the highest Ca2+ conductor of the ACh receptors implicated in innate immunity. Understanding the mechanism of how CHRFAM7A affects the immune system remains unexplored. METHODS Two model systems are used, human induced pluripotent stem cells (iPSC) and human primary monocytes, to characterize α7 nAChR function, Ca2+ dynamics and decoders to elucidate the pathway from receptor to phenotype. FINDINGS CHRFAM7A/α7 nAChR is identified as a hypomorphic receptor with mitigated Ca2+ influx and prolonged channel closed state. This shifts the Ca2+ reservoir from the extracellular space to the endoplasmic reticulum (ER) leading to Ca2+ dynamic changes. Ca2+ decoder small GTPase Rac1 is then activated, reorganizing the actin cytoskeleton. Observed actin mediated phenotypes include cellular adhesion, motility, phagocytosis and tissue mechanosensation. INTERPRETATION CHRFAM7A introduces an additional, human specific, layer to Ca2+ regulation leading to an innate immune gain of function. Through the actin cytoskeleton it drives adaptation to the mechanical properties of the tissue environment leading to an ability to invade previously immune restricted niches. Human genetic diversity predicts profound translational significance as its understanding builds the foundation for successful treatments for infectious diseases, sepsis, and cancer metastasis. FUNDING This work is supported in part by the Community Foundation for Greater Buffalo (Kinga Szigeti) and in part by NIH grant R01HL163168 (Yongho Bae).
Collapse
Affiliation(s)
- Kinga Szigeti
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA.
| | - Ivanna Ihnatovych
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Emily Notari
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Ryu P Dorn
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Ivan Maly
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Muye He
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Barbara Birkaya
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Shreyas Prasad
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Robin Schwartz Byrne
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Dinesh C Indurthi
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Erik Nimmer
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Yuna Heo
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Kolos Retfalvi
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Lee Chaves
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Norbert Sule
- Roswell Park Comprehensive Cancer Center, 665 Elm St, Buffalo, NY, 14203, USA
| | - Wilma A Hofmann
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Anthony Auerbach
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Gregory Wilding
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Yongho Bae
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Jessica Reynolds
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| |
Collapse
|
5
|
Lisec B, Bozic T, Santek I, Markelc B, Vrecl M, Frangez R, Cemazar M. Characterization of two distinct immortalized endothelial cell lines, EA.hy926 and HMEC-1, for in vitro studies: exploring the impact of calcium electroporation, Ca 2+ signaling and transcriptomic profiles. Cell Commun Signal 2024; 22:118. [PMID: 38347539 PMCID: PMC10863159 DOI: 10.1186/s12964-024-01503-2] [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: 11/08/2023] [Accepted: 01/28/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Disruption of Ca2+ homeostasis after calcium electroporation (CaEP) in tumors has been shown to elicit an enhanced antitumor effect with varying impacts on healthy tissue, such as endothelium. Therefore, our study aimed to determine differences in Ca2+ kinetics and gene expression involved in the regulation of Ca2+ signaling and homeostasis, as well as effects of CaEP on cytoskeleton and adherens junctions of the established endothelial cell lines EA.hy926 and HMEC-1. METHODS CaEP was performed on EA.hy926 and HMEC-1 cells with increasing Ca2+ concentrations. Viability after CaEP was assessed using Presto Blue, while the effect on cytoskeleton and adherens junctions was evaluated via immunofluorescence staining (F-actin, α-tubulin, VE-cadherin). Differences in intracellular Ca2+ regulation ([Ca2+]i) were determined with spectrofluorometric measurements using Fura-2-AM, exposing cells to DPBS, ionomycin, thapsigargin, ATP, bradykinin, angiotensin II, acetylcholine, LaCl3, and GdCl3. Molecular distinctions were identified by analyzing differentially expressed genes and pathways related to the cytoskeleton and Ca2+ signaling through RNA sequencing. RESULTS EA.hy926 cells, at increasing Ca2+ concentrations, displayed higher CaEP susceptibility and lower survival than HMEC-1. Immunofluorescence confirmed CaEP-induced, time- and Ca2+-dependent morphological changes in EA.hy926's actin filaments, microtubules, and cell-cell junctions. Spectrofluorometric Ca2+ kinetics showed higher amplitudes in Ca2+ responses in EA.hy926 exposed to buffer, G protein coupled receptor agonists, bradykinin, and angiotensin II compared to HMEC-1. HMEC-1 exhibited significantly higher [Ca2+]i changes after ionomycin exposure, while responses to thapsigargin, ATP, and acetylcholine were similar in both cell lines. ATP without extracellular Ca2+ ions induced a significantly higher [Ca2+]i rise in EA.hy926, suggesting purinergic ionotropic P2X and metabotropic P2Y receptor activation. RNA-sequencing analysis showed significant differences in cytoskeleton- and Ca2+-related gene expression, highlighting upregulation of ORAI2, TRPC1, TRPM2, CNGA3, TRPM6, and downregulation of TRPV4 and TRPC4 in EA.hy926 versus HMEC-1. Moreover, KEGG analysis showed upregulated Ca2+ import and downregulated export genes in EA.hy926. CONCLUSIONS Our finding show that significant differences in CaEP response and [Ca2+]i regulation exist between EA.hy926 and HMEC-1, which may be attributed to distinct transcriptomic profiles. EA.hy926, compared to HMEC-1, displayed higher susceptibility and sensitivity to [Ca2+]i changes, which may be linked to overexpression of Ca2+-related genes and an inability to mitigate changes in [Ca2+]i. The study offers a bioinformatic basis for selecting EC models based on research objectives.
Collapse
Affiliation(s)
- Barbara Lisec
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Zaloska cesta 2, SI-1000, Ljubljana, Slovenia
| | - Tim Bozic
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Zaloska cesta 2, SI-1000, Ljubljana, Slovenia
| | - Iva Santek
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Zaloska cesta 2, SI-1000, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, SI-1000, Ljubljana, Slovenia
| | - Bostjan Markelc
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Zaloska cesta 2, SI-1000, Ljubljana, Slovenia
| | - Milka Vrecl
- Institute of Preclinical Sciences, Veterinary Faculty, University of Ljubljana, Gerbiceva 60, SI-1000, Ljubljana, Slovenia
| | - Robert Frangez
- Institute of Preclinical Sciences, Veterinary Faculty, University of Ljubljana, Gerbiceva 60, SI-1000, Ljubljana, Slovenia
| | - Maja Cemazar
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Zaloska cesta 2, SI-1000, Ljubljana, Slovenia.
- Faculty of Health Sciences, University of Primorska, Polje 42, SI-6310, Izola, Slovenia.
| |
Collapse
|
6
|
Goetz A, Akl H, Dixit P. The ability to sense the environment is heterogeneously distributed in cell populations. eLife 2024; 12:RP87747. [PMID: 38293960 PMCID: PMC10942581 DOI: 10.7554/elife.87747] [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: 02/01/2024] Open
Abstract
Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information-theoretic framework to quantify the distribution of sensing abilities from single-cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an 'average cell'. We verify these predictions using live-cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information-theoretic framework will significantly improve our understanding of how cells sense in their environment.
Collapse
Affiliation(s)
- Andrew Goetz
- Department of Biomedical Engineering, Yale UniversityNew HavenUnited States
| | - Hoda Akl
- Department of Physics, University of FloridaGainesvilleUnited States
| | - Purushottam Dixit
- Department of Biomedical Engineering, Yale UniversityNew HavenUnited States
- Systems Biology Institute, Yale UniversityWest HavenUnited States
| |
Collapse
|
7
|
Li Z, Hu O, Xu S, Lin C, Yu W, Ma D, Lu J, Liu P. The SIRT3-ATAD3A axis regulates MAM dynamics and mitochondrial calcium homeostasis in cardiac hypertrophy. Int J Biol Sci 2024; 20:831-847. [PMID: 38250153 PMCID: PMC10797690 DOI: 10.7150/ijbs.89253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/18/2023] [Indexed: 01/23/2024] Open
Abstract
Mitochondria are energy-producing organelles that are mobile and harbor dynamic network structures. Although mitochondria and endoplasmic reticulum (ER) play distinct cellular roles, they are physically connected to maintain functional homeostasis. Abnormal changes in this interaction have been linked to pathological states, including cardiac hypertrophy. However, the exact regulatory molecules and mechanisms are yet to be elucidated. Here, we report that ATPase family AAA-domain containing protein 3A (ATAD3A) is an essential regulator of ER-mitochondria interplay within the mitochondria-associated membrane (MAM). ATAD3A prevents isoproterenol (ISO)-induced mitochondrial calcium accumulation, improving mitochondrial dysfunction and ER stress, which preserves cardiac function and attenuates cardiac hypertrophy. We also find that ATAD3A is a new substrate of NAD+-dependent deacetylase Sirtuin 3 (SIRT3). Notably, the heart mitochondria of SIRT3 knockout mice exhibited excessive formation of MAMs. Mechanistically, ATAD3A specifically undergoes acetylation, which reduces self-oligomerization and promotes cardiac hypertrophy. ATAD3A oligomerization is disrupted by acetylation at K134 site, and ATAD3A monomer closely interacts with the IP3R1-GRP75-VDAC1 complex, which leads to mitochondrial calcium overload and dysfunction. In summary, ATAD3A localizes to the MAMs, where it protects the homeostasis of ER-mitochondria contacts, quenching mitochondrial calcium overload and keeping mitochondrial bioenergetics unresponsive to ER stress. The SIRT3-ATAD3A axis represents a potential therapeutic target for cardiac hypertrophy.
Collapse
Affiliation(s)
- Zeyu Li
- National and Local United Engineering Lab of Druggability and New Drugs Evaluation, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Ou Hu
- Guangdong Province Engineering Laboratory for Druggability and New Drug Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Suowen Xu
- Department of Endocrinology, Institute of Endocrine and Metabolic Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Clinical Research Hospital of Chinese Academy of Sciences (Hefei), University of Science and Technology of China, Hefei, 230001, China
| | - Chenjia Lin
- National and Local United Engineering Lab of Druggability and New Drugs Evaluation, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Wenjing Yu
- National and Local United Engineering Lab of Druggability and New Drugs Evaluation, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Dinghu Ma
- National and Local United Engineering Lab of Druggability and New Drugs Evaluation, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Jing Lu
- National and Local United Engineering Lab of Druggability and New Drugs Evaluation, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Peiqing Liu
- National and Local United Engineering Lab of Druggability and New Drugs Evaluation, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| |
Collapse
|
8
|
Parres-Gold J, Levine M, Emert B, Stuart A, Elowitz MB. Principles of Computation by Competitive Protein Dimerization Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564854. [PMID: 37961250 PMCID: PMC10634983 DOI: 10.1101/2023.10.30.564854] [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
Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.
Collapse
Affiliation(s)
- Jacob Parres-Gold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Matthew Levine
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Benjamin Emert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Andrew Stuart
- Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Michael B. Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| |
Collapse
|
9
|
Dhyani V, George K, Gare S, Venkatesh KV, Mitra K, Giri L. A computational model to uncover the biophysical underpinnings of neural firing heterogeneity in dissociated hippocampal cultures. Hippocampus 2023; 33:1208-1227. [PMID: 37705290 DOI: 10.1002/hipo.23575] [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: 09/01/2022] [Revised: 07/12/2023] [Accepted: 08/21/2023] [Indexed: 09/15/2023]
Abstract
Calcium (Ca2+ ) imaging reveals a variety of correlated firing in cultures of dissociated hippocampal neurons, pinpointing the non-synaptic paracrine release of glutamate as a possible mediator for such firing patterns, although the biophysical underpinnings remain unknown. An intriguing possibility is that extracellular glutamate could bind metabotropic receptors linked with inositol trisphosphate (IP3 ) mediated release of Ca2+ from the endoplasmic reticulum of individual neurons, thereby modulating neural activity in combination with sarco/endoplasmic reticulum Ca2+ transport ATPase (SERCA) and voltage-gated Ca2+ channels (VGCC). However, the possibility that such release may occur in different neuronal compartments and can be inherently stochastic poses challenges in the characterization of such interplay between various Ca2+ channels. Here we deploy biophysical modeling in association with Monte Carlo parameter sampling to characterize such interplay and successfully predict experimentally observed Ca2+ patterns. The results show that the neurotransmitter level at the plasma membrane is the extrinsic source of heterogeneity in somatic Ca2+ transients. Our analysis, in particular, identifies the origin of such heterogeneity to an intrinsic differentiation of hippocampal neurons in terms of multiple cellular properties pertaining to intracellular Ca2+ signaling, such as VGCC, IP3 receptor, and SERCA expression. In the future, the biophysical model and parameter estimation approach used in this study can be upgraded to predict the response of a system of interconnected neurons.
Collapse
Affiliation(s)
- Vaibhav Dhyani
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India
- Optical Science Centre, Faculty of Science, Engineering & Technology, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Kevin George
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India
| | - Suman Gare
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, India
| | - Kishalay Mitra
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India
| |
Collapse
|
10
|
Gao X, Zhou P, Li F. The multiple activations in budding yeast S-phase checkpoint are Poisson processes. PNAS NEXUS 2023; 2:pgad342. [PMID: 37941810 PMCID: PMC10629469 DOI: 10.1093/pnasnexus/pgad342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/11/2023] [Indexed: 11/10/2023]
Abstract
Eukaryotic cells activate the S-phase checkpoint signal transduction pathway in response to DNA replication stress. Affected by the noise in biochemical reactions, such activation process demonstrates cell-to-cell variability. Here, through the analysis of microfluidics-integrated time-lapse imaging, we found multiple S-phase checkpoint activations in a certain budding yeast cell cycle. Yeast cells not only varied in their activation moments but also differed in the number of activations within the cell cycle, resulting in a stochastic multiple activation process. By investigating dynamics at the single-cell level, we showed that stochastic waiting times between consecutive activations are exponentially distributed and independent from each other. Finite DNA replication time provides a robust upper time limit to the duration of multiple activations. The mathematical model, together with further experimental evidence from the mutant strain, revealed that the number of activations under different levels of replication stress agreed well with Poisson distribution. Therefore, the activation events of S-phase checkpoint meet the criterion of Poisson process during DNA replication. In sum, the observed Poisson activation process may provide new insights into the complex stochastic dynamics of signal transduction pathways.
Collapse
Affiliation(s)
- Xin Gao
- School of Physics, Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, CA 92697, USA
| | - Fangting Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing 100871, China
| |
Collapse
|
11
|
Szigeti K, Ihnatovych I, Rosas N, Dorn RP, Notari E, Cortes Gomez E, He M, Maly I, Prasad S, Nimmer E, Heo Y, Fuchsova B, Bennett DA, Hofmann WA, Pralle A, Bae Y, Wang J. Neuronal actin cytoskeleton gain of function in the human brain. EBioMedicine 2023; 95:104725. [PMID: 37517100 PMCID: PMC10404607 DOI: 10.1016/j.ebiom.2023.104725] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/21/2023] [Accepted: 07/07/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND While advancements in imaging techniques have led to major strides in deciphering the human brain, successful interventions are elusive and represent some of the most persistent translational gaps in medicine. Human restricted CHRFAM7A has been associated with neuropsychiatric disorders. METHODS The physiological role of CHRFAM7A in human brain is explored using multiomics approach on 600 post mortem human brain tissue samples. The emerging pathways and mechanistic hypotheses are tested and validated in an isogenic hiPSC model of CHRFAM7A knock-in medial ganglionic eminence progenitors and neurons. FINDINGS CHRFAM7A is identified as a modulator of intracellular calcium dynamics and an upstream regulator of Rac1. Rac1 activation re-designs the actin cytoskeleton leading to dynamic actin driven remodeling of membrane protrusion and a switch from filopodia to lamellipodia. The reinforced cytoskeleton leads to an advantage to tolerate stiffer mechanical properties of the extracellular environment. INTERPRETATION CHRFAM7A modifies the actin cytoskeleton to a more dynamic and stiffness resistant state in an α7nAChR dependent manner. CHRFAM7A may facilitate neuronal adaptation to changes in the brain environment in physiological and pathological conditions contributing to risk or recovery. Understanding how CHRFAM7A affects human brain requires human studies in the areas of memory formation and erasure, cognitive reserve, and neuronal plasticity. FUNDING This work is supported in part by the Community Foundation for Greater Buffalo (Kinga Szigeti). Also, in part by the International Society for Neurochemistry (ISN) and The Company of Biologists (Nicolas Rosas). ROSMAP is supported by NIA grants P30AG10161, P30AG72975, R01AG15819, R01AG17917. U01AG46152, and U01AG61356.
Collapse
Affiliation(s)
- Kinga Szigeti
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA.
| | - Ivanna Ihnatovych
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Nicolás Rosas
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA; Instituto de Investigaciones Biotecnológicas, Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de, Investigaciones Científicas y Técnicas (CONICET), San Martín, Buenos Aires, Argentina
| | - Ryu P Dorn
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Emily Notari
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | | | - Muye He
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Ivan Maly
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Shreyas Prasad
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Erik Nimmer
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Yuna Heo
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Beata Fuchsova
- Instituto de Investigaciones Biotecnológicas, Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de, Investigaciones Científicas y Técnicas (CONICET), San Martín, Buenos Aires, Argentina
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Wilma A Hofmann
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Arnd Pralle
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Yongho Bae
- State University of New York at Buffalo, 875 Ellicott St., Buffalo, NY, 14203, USA
| | - Jianmin Wang
- Roswell Park Comprehensive Cancer Center, 665 Elm St, Buffalo, NY 14203, USA
| |
Collapse
|
12
|
Kumar N, Dowling A, Zartman J. Reverse engineering morphogenesis through Bayesian optimization of physics-based models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.553928. [PMID: 37662294 PMCID: PMC10473585 DOI: 10.1101/2023.08.21.553928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Morphogenetic programs direct the cell signaling and nonlinear mechanical interactions between multiple cell types and tissue layers to define organ shape and size. A key challenge for systems and synthetic biology is determining optimal combinations of intra- and inter-cellular interactions to predict an organ's shape, size, and function. Physics-based mechanistic models that define the subcellular force distribution facilitate this, but it is extremely challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the desired organ shapes observed within the experimental imaging data. This integrative framework employs Gaussian Process Regression (GPR), a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that generate and maintain the final organ shape. We calibrated and tested the method on cross-sections of Drosophila wing imaginal discs, a highly informative model organ system, to study mechanisms that regulate epithelial processes that range from development to cancer. As a specific test case, the parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with time series imaging data of wing discs perturbed with collagenase. Unexpectedly, the framework also identifies multiple distinct parameter sets that generate shapes similar to wild-type organ shapes. This platform enables an efficient, global sensitivity analysis to support the necessity of both actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with fixed tissue imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This framework is extensible toward reverse-engineering the morphogenesis of any organ system and can be utilized in real-time control of complex multicellular systems.
Collapse
|
13
|
Kana O, Nault R, Filipovic D, Marri D, Zacharewski T, Bhattacharya S. Generative modeling of single-cell gene expression for dose-dependent chemical perturbations. PATTERNS (NEW YORK, N.Y.) 2023; 4:100817. [PMID: 37602218 PMCID: PMC10436058 DOI: 10.1016/j.patter.2023.100817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/07/2022] [Accepted: 07/14/2023] [Indexed: 08/22/2023]
Abstract
Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and multiple-dose cellular responses better than existing models. We show that scVIDR can predict dose-dependent gene expression across mouse hepatocytes, human blood cells, and cancer cell lines. We biologically interpret the latent space of scVIDR using a regression model and use scVIDR to order individual cells based on their sensitivity to chemical perturbation by assigning each cell a "pseudo-dose" value. We envision that scVIDR can help reduce the need for repeated animal testing across tissues, chemicals, and doses.
Collapse
Affiliation(s)
- Omar Kana
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, USA
- Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Rance Nault
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology Michigan State University, Michigan State University, East Lansing, MI 48824, USA
| | - David Filipovic
- Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Daniel Marri
- Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Tim Zacharewski
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology Michigan State University, Michigan State University, East Lansing, MI 48824, USA
| | - Sudin Bhattacharya
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, USA
- Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
| |
Collapse
|
14
|
Venkatachalapathy H, Brzakala C, Batchelor E, Azarin SM, Sarkar CA. Inertial effect of cell state velocity on the quiescence-proliferation fate decision in breast cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541793. [PMID: 37292599 PMCID: PMC10245870 DOI: 10.1101/2023.05.22.541793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Energy landscapes can provide intuitive depictions of population heterogeneity and dynamics. However, it is unclear whether individual cell behavior, hypothesized to be determined by initial position and noise, is faithfully recapitulated. Using the p21-/Cdk2-dependent quiescence-proliferation decision in breast cancer dormancy as a testbed, we examined single-cell dynamics on the landscape when perturbed by hypoxia, a dormancy-inducing stress. Combining trajectory-based energy landscape generation with single-cell time-lapse microscopy, we found that initial position on a p21/Cdk2 landscape did not fully explain the observed cell-fate heterogeneity under hypoxia. Instead, cells with higher cell state velocities prior to hypoxia, influenced by epigenetic parameters, tended to remain proliferative under hypoxia. Thus, the fate decision on this landscape is significantly influenced by "inertia", a velocity-dependent ability to resist directional changes despite reshaping of the underlying landscape, superseding positional effects. Such inertial effects may markedly influence cell-fate trajectories in tumors and other dynamically changing microenvironments.
Collapse
Affiliation(s)
- Harish Venkatachalapathy
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Cole Brzakala
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Eric Batchelor
- Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Samira M. Azarin
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Casim A. Sarkar
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| |
Collapse
|
15
|
Ho KKY, Srivastava S, Kinnunen PC, Garikipati K, Luker GD, Luker KE. Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways. Bioengineering (Basel) 2023; 10:bioengineering10020269. [PMID: 36829763 PMCID: PMC9952091 DOI: 10.3390/bioengineering10020269] [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: 12/26/2022] [Revised: 01/24/2023] [Accepted: 02/12/2023] [Indexed: 02/22/2023] Open
Abstract
Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.
Collapse
Affiliation(s)
- Kenneth K. Y. Ho
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Siddhartha Srivastava
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Patrick C. Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Krishna Garikipati
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Computational Discovery & Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gary D. Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence: (G.D.L.); (K.E.L.)
| | - Kathryn E. Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence: (G.D.L.); (K.E.L.)
| |
Collapse
|
16
|
Sarma U, Ripka L, Anyaegbunam UA, Legewie S. Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway. Methods Mol Biol 2023; 2634:215-251. [PMID: 37074581 DOI: 10.1007/978-1-0716-3008-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.
Collapse
Affiliation(s)
- Uddipan Sarma
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Lorenz Ripka
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Uchenna Alex Anyaegbunam
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Stefan Legewie
- Institute of Molecular Biology (IMB), Mainz, Germany.
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany.
- Stuttgart Research Center for Systems Biology, University of Stuttgart, Stuttgart, Germany.
| |
Collapse
|
17
|
Gare S, Chel S, Abhinav TK, Dhyani V, Jana S, Giri L. Mapping of structural arrangement of cells and collective calcium transients: an integrated framework combining live cell imaging using confocal microscopy and UMAP-assisted HDBSCAN-based approach. Integr Biol (Camb) 2022; 14:184-203. [PMID: 36670549 DOI: 10.1093/intbio/zyac017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 11/22/2022] [Accepted: 11/30/2022] [Indexed: 01/22/2023]
Abstract
Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.
Collapse
Affiliation(s)
- Suman Gare
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Soumita Chel
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - T K Abhinav
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Vaibhav Dhyani
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Soumya Jana
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| |
Collapse
|
18
|
Kinnunen PC, Luker GD, Luker KE, Linderman JJ. Computational modeling implicates protein scaffolding in p38 regulation of Akt. J Theor Biol 2022; 555:111294. [PMID: 36195198 PMCID: PMC10394737 DOI: 10.1016/j.jtbi.2022.111294] [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: 06/24/2022] [Revised: 09/16/2022] [Accepted: 09/26/2022] [Indexed: 01/14/2023]
Abstract
Cells process environmental cues by activating intracellular signaling pathways with numerous interconnections and opportunities for cross-regulation. We employed a systems biology approach to investigate intersections of kinase p38, a context-dependent tumor suppressor or promoter, with Akt and ERK, two kinases known to promote cell survival, proliferation, and drug resistance in cancer. Using live, single cell microscopy, multiplexed fluorescent reporters of p38, Akt, and ERK activities, and a custom automated image-processing pipeline, we detected marked heterogeneity of signaling outputs in breast cancer cells stimulated with chemokine CXCL12 or epidermal growth factor (EGF). Basal activity of p38 correlated inversely with amplitude of Akt and ERK activation in response to either ligand. Remarkably, small molecule inhibitors of p38 immediately decreased basal activities of Akt and ERK but increased the proportion of cells with high amplitude ligand-induced activation of Akt signaling. To identify mechanisms underlying cross-talk of p38 with Akt signaling, we developed a computational model incorporating subcellular compartmentalization of signaling molecules by scaffold proteins. Dynamics of this model revealed that subcellular scaffolding of Akt accounted for observed regulation by p38. The model also predicted that differences in the amount of scaffold protein in a subcellular compartment captured the observed single cell heterogeneity in signaling. Finally, our model predicted that reduction in kinase signaling can be accomplished by both scaffolding and direct kinase inhibition. However, scaffolding inhibition can potentiate future kinase activity by redistribution of pathway components, potentially amplifying oncogenic signaling. These studies reveal how computational modeling can decipher mechanisms of cross-talk between the p38 and Akt signaling pathways and point to scaffold proteins as central regulators of signaling dynamics and amplitude.
Collapse
Affiliation(s)
- Patrick C Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109 United States
| | - Gary D Luker
- Department of Radiology and the Center for Molecular Imaging, University of Michigan School of Medicine, Ann Arbor, MI, 48109 United States; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109 United States; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, 48109 United States
| | - Kathryn E Luker
- Department of Radiology and the Center for Molecular Imaging, University of Michigan School of Medicine, Ann Arbor, MI, 48109 United States
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109 United States; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109 United States.
| |
Collapse
|
19
|
Maltz E, Wollman R. Quantifying the phenotypic information in mRNA abundance. Mol Syst Biol 2022; 18:e11001. [PMID: 35965452 PMCID: PMC9376724 DOI: 10.15252/msb.202211001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single‐cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca2+ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.
Collapse
Affiliation(s)
- Evan Maltz
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.,Institute of Quantitative and Computational Bioscience, UCLA, Los Angeles, CA, USA
| | - Roy Wollman
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.,Institute of Quantitative and Computational Bioscience, UCLA, Los Angeles, CA, USA.,Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA, USA
| |
Collapse
|
20
|
Kramer BA, Sarabia del Castillo J, Pelkmans L. Multimodal perception links cellular state to decision making in single cells. Science 2022; 377:642-648. [DOI: 10.1126/science.abf4062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Individual cells take decisions that are adapted to their internal state and surroundings, but how cells can reliably do this remains unclear. Using multiplexed quantification of signaling responses and markers of the cellular state, we find that signaling nodes in a network display adaptive information processing, which leads to heterogeneous growth factor responses and enables nodes to capture partially non-redundant information about the cellular state. Collectively, as a multimodal percept, this gives individual cells a large information processing capacity to accurately place growth factor concentration within the context of their cellular state and make cellular state-dependent decisions. We propose that heterogeneity and complexity in signaling networks have co-evolved to enable specific and context-aware cellular decision making in a multicellular setting.
Collapse
Affiliation(s)
- Bernhard A. Kramer
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
- Molecular Life Sciences PhD program, Life Science Zurich Graduate School, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Jacobo Sarabia del Castillo
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Lucas Pelkmans
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| |
Collapse
|
21
|
Topolewski P, Zakrzewska KE, Walczak J, Nienałtowski K, Müller-Newen G, Singh A, Komorowski M. Phenotypic variability, not noise, accounts for most of the cell-to-cell heterogeneity in IFN-γ and oncostatin M signaling responses. Sci Signal 2022; 15:eabd9303. [PMID: 35167339 DOI: 10.1126/scisignal.abd9303] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Cellular signaling responses show substantial cell-to-cell heterogeneity, which is often ascribed to the inherent randomness of biochemical reactions, termed molecular noise, wherein high noise implies low signaling fidelity. Alternatively, heterogeneity could arise from differences in molecular content between cells, termed molecular phenotypic variability, which does not necessarily imply imprecise signaling. The contribution of these two processes to signaling heterogeneity is unclear. Here, we fused fibroblasts to produce binuclear syncytia to distinguish noise from phenotypic variability in the analysis of cytokine signaling. We reasoned that the responses of the two nuclei within one syncytium could approximate the signaling outcomes of two cells with the same molecular content, thereby disclosing noise contribution, whereas comparison of different syncytia should reveal contribution of phenotypic variability. We found that ~90% of the variance in the primary response (which was the abundance of phosphorylated, nuclear STAT) to stimulation with the cytokines interferon-γ and oncostatin M resulted from differences in the molecular content of individual cells. Thus, our data reveal that cytokine signaling in the system used here operates in a reproducible, high-fidelity manner.
Collapse
Affiliation(s)
- Piotr Topolewski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Karolina E Zakrzewska
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Jarosław Walczak
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Karol Nienałtowski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Gerhard Müller-Newen
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, 52074 Aachen, Germany
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
| | - Michał Komorowski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| |
Collapse
|
22
|
Maly IV, Hofmann WA. Bayesian inference of molecular kinetic parameters from astrocyte calcium imaging data. MethodsX 2022; 9:101825. [PMID: 36110987 PMCID: PMC9468493 DOI: 10.1016/j.mex.2022.101825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/15/2022] [Indexed: 11/27/2022] Open
Abstract
Model-based Bayesian inference from high-content data obtained on live specimens is a burgeoning field with demonstrated applications to neuroscience. In parallel, computer vision methods for extracting the calcium signaling information from imaging data have advanced in application to neuronal physiology. Here, we are describing in detail a method we have recently developed to study calcium dynamics in astrocytes, which combines computer vision with model-based Bayesian learning to deduce the most likely molecular kinetic parameters underlying the observed calcium activity. As reported in the companion experimental study, this method allowed us to identify the key molecular changes downstream of a multi-gene deletion modeling the human 22q11.2 deletion syndrome, the most common human microdeletion and the genetic factor with the highest penetrance for schizophrenia.Methodological details are laid out, from our imaging approach to our adaptation of the VBA-CaBBI algorithm previously developed primarily for brain functional imaging data. The analytical pipeline is suited for further applications to glial cells and adaptable to other cell types exhibiting complexcalcium dynamics.
Collapse
|
23
|
Jones-Tabah J, Martin RD, Tanny JC, Clarke PBS, Hébert TE. High-Content Single-Cell Förster Resonance Energy Transfer Imaging of Cultured Striatal Neurons Reveals Novel Cross-Talk in the Regulation of Nuclear Signaling by Protein Kinase A and Extracellular Signal-Regulated Kinase 1/2. Mol Pharmacol 2021; 100:526-539. [PMID: 34503973 DOI: 10.1124/molpharm.121.000290] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 09/07/2021] [Indexed: 11/22/2022] Open
Abstract
Genetically encoded biosensors can be used to track signaling events in living cells by measuring changes in fluorescence emitted by one or more fluorescent proteins. Here, we describe the use of genetically encoded biosensors based on Förster resonance energy transfer (FRET), combined with high-content microscopy, to image dynamic signaling events simultaneously in thousands of neurons in response to drug treatments. We first applied this approach to examine intercellular variation in signaling responses among cultured striatal neurons stimulated with multiple drugs. Using high-content FRET imaging and immunofluorescence, we identified neuronal subpopulations with unique responses to pharmacological manipulation and used nuclear morphology to identify medium spiny neurons within these heterogeneous striatal cultures. Focusing on protein kinase A (PKA) and extracellular signal-regulated kinase 1/2 (ERK1/2) signaling in the cytoplasm and nucleus, we noted pronounced intercellular differences among putative medium spiny neurons, in both the magnitude and kinetics of signaling responses to drug application. Importantly, a conventional "bulk" analysis that pooled all cells in culture yielded a different rank order of drug potency than that revealed by single-cell analysis. Using a single-cell analytical approach, we dissected the relative contributions of PKA and ERK1/2 signaling in striatal neurons and unexpectedly identified a novel role for ERK1/2 in promoting nuclear activation of PKA in striatal neurons. This finding adds a new dimension of signaling crosstalk between PKA and ERK1/2 with relevance to dopamine D1 receptor signaling in striatal neurons. In conclusion, high-content single-cell imaging can complement and extend traditional population-level analyses and provides a novel vantage point from which to study cellular signaling. SIGNIFICANCE STATEMENT: High-content imaging revealed substantial intercellular variation in the magnitude and pattern of intracellular signaling events driven by receptor stimulation. Since individual neurons within the same population can respond differently to a given agonist, interpreting measures of intracellular signaling derived from the averaged response of entire neuronal populations may not always reflect what happened at the single-cell level. This study uses this approach to identify a new form of cross-talk between PKA and ERK1/2 signaling in the nucleus of striatal neurons.
Collapse
Affiliation(s)
- Jace Jones-Tabah
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Ryan D Martin
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Jason C Tanny
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Paul B S Clarke
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Terence E Hébert
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| |
Collapse
|
24
|
Kinnunen PC, Luker KE, Luker GD, Linderman JJ. Computational methods for characterizing and learning from heterogeneous cell signaling data. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 26:98-108. [PMID: 35647414 DOI: 10.1016/j.coisb.2021.04.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Heterogeneity in cell signaling pathways is increasingly appreciated as a fundamental feature of cell biology and a driver of clinically relevant disease phenotypes. Understanding the causes of heterogeneity, the cellular mechanisms used to control heterogeneity, and the downstream effects of heterogeneity in single cells are all key obstacles for manipulating cellular populations and treating disease. Recent advances in genetic engineering, including multiplexed fluorescent reporters, have provided unprecedented measurements of signaling heterogeneity, but these vast data sets are often difficult to interpret, necessitating the use of computational techniques to extract meaning from the data. Here, we review recent advances in computational methods for extracting meaning from these novel data streams. In particular, we evaluate how machine learning methods related to dimensionality reduction and classification can identify structure in complex, dynamic datasets, simplifying interpretation. We also discuss how mechanistic models can be merged with heterogeneous data to understand the underlying differences between cells in a population. These methods are still being developed, but the work reviewed here offers useful applications of specific analysis techniques that could enable the translation of single-cell signaling data to actionable biological understanding.
Collapse
Affiliation(s)
- Patrick C Kinnunen
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI, 48109-2800, USA
| | - Kathryn E Luker
- Department of Radiology, Center for Molecular Imaging, University of Michigan, 109 Zina Pitcher Place, A526 BSRB, Ann Arbor, MI, 48109-2200, USA
| | - Gary D Luker
- Department of Radiology, Center for Molecular Imaging, University of Michigan, 109 Zina Pitcher Place, A526 BSRB, Ann Arbor, MI, 48109-2200, USA.,Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI, USA, 48109.,Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA, 48109
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI, 48109-2800, USA.,Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI, USA, 48109
| |
Collapse
|
25
|
Spinosa PC, Kinnunen PC, Humphries BA, Luker GD, Luker KE, Linderman JJ. Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling. Cell Mol Bioeng 2021; 14:49-64. [PMID: 33643466 PMCID: PMC7878609 DOI: 10.1007/s12195-020-00640-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/22/2020] [Indexed: 10/23/2022] Open
Abstract
INTRODUCTION CXCR4 and epidermal growth factor receptor (EGFR) represent two major families of receptors, G-protein coupled receptors and receptor tyrosine kinases, with central functions in cancer. While utilizing different upstream signaling molecules, both CXCR4 and EGFR activate kinases ERK and Akt, although single-cell activation of these kinases is markedly heterogeneous. One hypothesis regarding the origin of signaling heterogeneity proposes that intercellular variations arise from differences in pre-existing intracellular states set by extrinsic noise. While pre-existing cell states vary among cells, each pre-existing state defines deterministic signaling outputs to downstream effectors. Understanding causes of signaling heterogeneity will inform treatment of cancers with drugs targeting drivers of oncogenic signaling. METHODS We built a single-cell computational model to predict Akt and ERK responses to CXCR4- and EGFR-mediated stimulation. We investigated signaling heterogeneity through these receptors and tested model predictions using quantitative, live-cell time-lapse imaging. RESULTS We show that the pre-existing cell state predicts single-cell signaling through both CXCR4 and EGFR. Computational modeling reveals that the same set of pre-existing cell states explains signaling heterogeneity through both EGFR and CXCR4 at multiple doses of ligands and in two different breast cancer cell lines. The model also predicts how phosphatidylinositol-3-kinase (PI3K) targeted therapies potentiate ERK signaling in certain breast cancer cells and that low level, combined inhibition of MEK and PI3K ablates potentiated ERK signaling. CONCLUSIONS Our data demonstrate that a conserved motif exists for EGFR and CXCR4 signaling and suggest potential clinical utility of the computational model to optimize therapy.
Collapse
Affiliation(s)
- Phillip C. Spinosa
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109-2800 USA
| | - Patrick C. Kinnunen
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109-2800 USA
| | - Brock A. Humphries
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Gary D. Luker
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI USA 48109
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI USA 48109
| | - Kathryn E. Luker
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Radiology, Center for Molecular Imaging, University of Michigan, 109 Zina Pitcher Place, A526 BSRB, Ann Arbor, MI 48109-2200 USA
| | - Jennifer J. Linderman
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109-2800 USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI USA 48109
| |
Collapse
|
26
|
Qi H, Li X, Jin Z, Simmen T, Shuai J. The Oscillation Amplitude, Not the Frequency of Cytosolic Calcium, Regulates Apoptosis Induction. iScience 2020; 23:101671. [PMID: 33196017 PMCID: PMC7644924 DOI: 10.1016/j.isci.2020.101671] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/15/2020] [Accepted: 10/08/2020] [Indexed: 01/06/2023] Open
Abstract
Although a rising concentration of cytosolic Ca2+ has long been recognized as an essential signal for apoptosis, the dynamical mechanisms by which Ca2+ regulates apoptosis are not clear yet. To address this, we constructed a computational model that integrates known biochemical reactions and can reproduce the dynamical behaviors of Ca2+-induced apoptosis as observed in experiments. Model analysis shows that oscillating Ca2+ signals first convert into gradual signals and eventually transform into a switch-like apoptotic response. Via the two processes, the apoptotic signaling pathway filters the frequency of Ca2+ oscillations effectively but instead responds acutely to their amplitude. Collectively, our results suggest that Ca2+ regulates apoptosis mainly via oscillation amplitude, rather than frequency, modulation. This study not only provides a comprehensive understanding of how oscillatory Ca2+ dynamically regulates the complex apoptotic signaling network but also presents a typical example of how Ca2+ controls cellular responses through amplitude modulation.
Collapse
Affiliation(s)
- Hong Qi
- Complex Systems Research Center, Shanxi University, Taiyuan 030006, China.,Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, China
| | - Xiang Li
- Department of Physics, Xiamen University, Xiamen 361005, China.,State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen 361102, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361102, China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Taiyuan 030006, China.,Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, China
| | - Thomas Simmen
- Faculty of Medicine and Dentistry, Department of Cell Biology, University of Alberta, Edmonton, AB T6G2H7, Canada
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen 361005, China.,State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen 361102, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361102, China
| |
Collapse
|
27
|
Maity A, Wollman R. Information transmission from NFkB signaling dynamics to gene expression. PLoS Comput Biol 2020; 16:e1008011. [PMID: 32797040 PMCID: PMC7478807 DOI: 10.1371/journal.pcbi.1008011] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 09/08/2020] [Accepted: 06/02/2020] [Indexed: 02/06/2023] Open
Abstract
The dynamic signal encoding paradigm suggests that information flows from the extracellular environment into specific signaling patterns (encoding) that are then read by downstream effectors to control cellular behavior. Previous work empirically quantified the information content of dynamic signaling patterns. However, whether this information can be faithfully transmitted to the gene expression level is unclear. Here we used NFkB signaling as a model to understand the accuracy of information transmission from signaling dynamics into gene expression. Using a detailed mathematical model, we simulated realistic NFkB signaling patterns with different degrees of variability. The NFkB patterns were used as an input to a simple gene expression model. Analysis of information transmission between ligand and NFkB and ligand and gene expression allows us to determine information loss in transmission between receptors to dynamic signaling patterns and between signaling dynamics to gene expression. Information loss could occur due to biochemical noise or due to a lack of specificity. We found that noise-free gene expression has very little information loss suggesting that gene expression can preserve specificity in NFkB patterns. As expected, the addition of noise to the gene expression model results in information loss. Interestingly, this effect can be mitigated by a specific choice of parameters that can substantially reduce information loss due to biochemical noise during gene expression. Overall our results show that the cellular capacity for information transmission from dynamic signaling patterns to gene expression can be high enough to preserve ligand specificity and thereby the accuracy of cellular response to environmental cues.
Collapse
Affiliation(s)
- Alok Maity
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America
| | - Roy Wollman
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America
- Departments of Integrative Biology and Physiology and Chemistry and Biochemistry, University of California UCLA, California, United States of America
- * E-mail:
| |
Collapse
|
28
|
Lee D, Jayaraman A, Kwon JS. Identification of cell‐to‐cell heterogeneity through systems engineering approaches. AIChE J 2020. [DOI: 10.1002/aic.16925] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Dongheon Lee
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
| | - Joseph S.‐I. Kwon
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
| |
Collapse
|
29
|
Foreman R, Wollman R. Mammalian gene expression variability is explained by underlying cell state. Mol Syst Biol 2020; 16:e9146. [PMID: 32043799 PMCID: PMC7011657 DOI: 10.15252/msb.20199146] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 12/04/2019] [Accepted: 01/07/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.
Collapse
Affiliation(s)
- Robert Foreman
- Institute for Quantitative and Computational BiosciencesUniversity of California, Los AngelesLos AngelesCAUSA
- Program in Bioinformatics and Systems BiologyUniversity of California, San DiegoSan DiegoCAUSA
| | - Roy Wollman
- Institute for Quantitative and Computational BiosciencesUniversity of California, Los AngelesLos AngelesCAUSA
- Program in Bioinformatics and Systems BiologyUniversity of California, San DiegoSan DiegoCAUSA
- Department of Integrative Biology and PhysiologyDepartment of Chemistry and BiochemistryUniversity of California, Los AngelesLos AngelesCAUSA
| |
Collapse
|
30
|
Meyer AS, Heiser LM. Systems biology approaches to measure and model phenotypic heterogeneity in cancer. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 17:35-40. [PMID: 32864511 PMCID: PMC7449235 DOI: 10.1016/j.coisb.2019.09.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The recent wide-spread adoption of single cell profiling technologies has revealed that individual cancers are not homogenous collections of deregulated cells, but instead are comprised of multiple genetically and phenotypically distinct cell subpopulations that exhibit a wide range of responses to extracellular signals and therapeutic insult. Such observations point to the urgent need to understand cancer as a complex, adaptive system. Cancer systems biology studies seek to develop the experimental and theoretical methods required to understand how biological components work together to determine how cancer cells function. Ultimately, such approaches will lead to improvements in how cancer is managed and treated. In this review, we discuss recent advances in cancer systems biology approaches to quantify, model, and elucidate mechanisms of heterogeneity.
Collapse
Affiliation(s)
- Aaron S. Meyer
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Laura M. Heiser
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine, OHSU, Portland, OR, USA
| |
Collapse
|
31
|
Spinosa PC, Humphries BA, Lewin Mejia D, Buschhaus JM, Linderman JJ, Luker GD, Luker KE. Short-term cellular memory tunes the signaling responses of the chemokine receptor CXCR4. Sci Signal 2019; 12:eaaw4204. [PMID: 31289212 PMCID: PMC7059217 DOI: 10.1126/scisignal.aaw4204] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The chemokine receptor CXCR4 regulates fundamental processes in development, normal physiology, and diseases, including cancer. Small subpopulations of CXCR4-positive cells drive the local invasion and dissemination of malignant cells during metastasis, emphasizing the need to understand the mechanisms controlling responses at the single-cell level to receptor activation by the chemokine ligand CXCL12. Using single-cell imaging, we discovered that short-term cellular memory of changes in environmental conditions tuned CXCR4 signaling to Akt and ERK, two kinases activated by this receptor. Conditioning cells with growth stimuli before CXCL12 exposure increased the number of cells that initiated CXCR4 signaling and the amplitude of Akt and ERK activation. Data-driven, single-cell computational modeling revealed that growth factor conditioning modulated CXCR4-dependent activation of Akt and ERK by decreasing extrinsic noise (preexisting cell-to-cell differences in kinase activity) in PI3K and mTORC1. Modeling established mTORC1 as critical for tuning single-cell responses to CXCL12-CXCR4 signaling. Our single-cell model predicted how combinations of extrinsic noise in PI3K, Ras, and mTORC1 superimposed on different driver mutations in the ERK and/or Akt pathways to bias CXCR4 signaling. Computational experiments correctly predicted that selected kinase inhibitors used for cancer therapy shifted subsets of cells to states that were more permissive to CXCR4 activation, suggesting that such drugs may inadvertently potentiate pro-metastatic CXCR4 signaling. Our work establishes how changing environmental inputs modulate CXCR4 signaling in single cells and provides a framework to optimize the development and use of drugs targeting this signaling pathway.
Collapse
Affiliation(s)
- Phillip C Spinosa
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Brock A Humphries
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Daniela Lewin Mejia
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Johanna M Buschhaus
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Gary D Luker
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Kathryn E Luker
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| |
Collapse
|
32
|
Calcium Activity Dynamics Correlate with Neuronal Phenotype at a Single Cell Level and in a Threshold-Dependent Manner. Int J Mol Sci 2019; 20:ijms20081880. [PMID: 30995769 PMCID: PMC6515432 DOI: 10.3390/ijms20081880] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 04/08/2019] [Accepted: 04/10/2019] [Indexed: 12/23/2022] Open
Abstract
Calcium is a ubiquitous signaling molecule that plays a vital role in many physiological processes. Recent work has shown that calcium activity is especially critical in vertebrate neural development. Here, we investigated if calcium activity and neuronal phenotype are correlated only on a population level or on the level of single cells. Using Xenopus primary cell culture in which individual cells can be unambiguously identified and associated with a molecular phenotype, we correlated calcium activity with neuronal phenotype on the single-cell level. This analysis revealed that, at the neural plate stage, a high frequency of low-amplitude spiking activity correlates with an excitatory, glutamatergic phenotype, while high-amplitude spiking activity correlates with an inhibitory, GABAergic phenotype. Surprisingly, we also found that high-frequency, low-amplitude spiking activity correlates with neural progenitor cells and that differentiating cells exhibit higher spike amplitude. Additional methods of analysis suggested that differentiating marker tubb2b-expressing cells exhibit relatively persistent and predictable calcium activity compared to the irregular activity of neural progenitor cells. Our study highlights the value of using a range of thresholds for analyzing calcium activity data and underscores the importance of employing multiple methods to characterize the often irregular, complex patterns of calcium activity during early neural development.
Collapse
|
33
|
Sumit M, Jovic A, Neubig RR, Takayama S, Linderman JJ. A Two-Pulse Cellular Stimulation Test Elucidates Variability and Mechanisms in Signaling Pathways. Biophys J 2019; 116:962-973. [PMID: 30782397 DOI: 10.1016/j.bpj.2019.01.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/14/2019] [Accepted: 01/18/2019] [Indexed: 12/14/2022] Open
Abstract
Mammalian cells respond in a variable manner when provided with physiological pulses of ligand, such as low concentrations of acetylcholine present for just tens of seconds or TNFα for just tens of minutes. For a two-pulse stimulation, some cells respond to both pulses, some do not respond, and yet others respond to only one or the other pulse. Are these different response patterns the result of the small number of ligands being able to only stochastically activate the pathway at random times or an output pattern from a deterministic algorithm responding differently to different stimulation intervals? If the response is deterministic in nature, what parameters determine whether a response is generated or skipped? To answer these questions, we developed a two-pulse test that utilizes different rest periods between stimulation pulses. This "rest-period test" revealed that cells skip responses predictably as the rest period is shortened. By combining these experimental results with a mathematical model of the pathway, we further obtained mechanistic insight into potential sources of response variability. Our analysis indicates that in both intracellular calcium and NFκB signaling, response variability is consistent with extrinsic noise (cell-to-cell variability in protein levels), a short-term memory of stimulation, and high Hill coefficient processes. Furthermore, these results support recent works that have emphasized the role of deterministic processes for explaining apparently stochastic cellular response variability and indicate that even weak stimulations likely guide mammalian cells to appropriate fates rather than leaving outcomes to chance. We envision that the rest-period test can be applied to other signaling pathways to extract mechanistic insight.
Collapse
Affiliation(s)
- Madhuresh Sumit
- Biophysics Graduate Program, University of Michigan, Ann Arbor, Michigan
| | - Andreja Jovic
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Richard R Neubig
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
| | - Shuichi Takayama
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, Georgia.
| | - Jennifer J Linderman
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan; Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan.
| |
Collapse
|
34
|
Fröhlich F, Loos C, Hasenauer J. Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes. Methods Mol Biol 2019; 1883:385-422. [PMID: 30547409 DOI: 10.1007/978-1-4939-8882-2_16] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.
Collapse
Affiliation(s)
- Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Carolin Loos
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- Center for Mathematics, Technische Universität München, Garching, Germany.
| |
Collapse
|
35
|
Polypyrimidine tract-binding protein blocks miRNA-124 biogenesis to enforce its neuronal-specific expression in the mouse. Proc Natl Acad Sci U S A 2018; 115:E11061-E11070. [PMID: 30401736 DOI: 10.1073/pnas.1809609115] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
MicroRNA (miRNA)-124 is expressed in neurons, where it represses genes inhibitory for neuronal differentiation, including the RNA binding protein PTBP1. PTBP1 maintains nonneuronal splicing patterns of mRNAs that switch to neuronal isoforms upon neuronal differentiation. We find that primary (pri)-miR-124-1 is expressed in mouse embryonic stem cells where mature miR-124 is absent. PTBP1 binds to this precursor RNA upstream of the miRNA stem-loop to inhibit mature miR-124 expression in vivo and DROSHA cleavage of pri-miR-124-1 in vitro. This function for PTBP1 in repressing miR-124 biogenesis defines an additional regulatory loop in the already intricate interplay between these two molecules. Applying mathematical modeling to examine the dynamics of this regulation, we find that the pool of pri-miR-124 whose maturation is blocked by PTBP1 creates a robust and self-reinforcing transition in gene expression as PTBP1 is depleted during early neuronal differentiation. While interlocking regulatory loops are often found between miRNAs and transcriptional regulators, our results indicate that miRNA targeting of posttranscriptional regulators also reinforces developmental decisions. Notably, induction of neuronal differentiation observed upon PTBP1 knockdown likely results from direct derepression of miR-124, in addition to indirect effects previously described.
Collapse
|
36
|
Kippner LE, Kemp ML. Oscillatory IL-2 stimulus reveals pertinent signaling timescales of T cell responsiveness. PLoS One 2018; 13:e0203759. [PMID: 30226854 PMCID: PMC6143248 DOI: 10.1371/journal.pone.0203759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 08/27/2018] [Indexed: 11/05/2022] Open
Abstract
Cell response to extracellular ligand is affected not only by ligand availability, but also by pre-existing cell-to-cell variability that enables a range of responses within a cell population. We developed a computational model that incorporates cell heterogeneity in order to investigate Jurkat T cell response to time dependent extracellular IL-2 stimulation. Our model predicted preferred timing of IL-2 oscillatory input for maximizing downstream intracellular STAT5 nuclear translocation. The modeled cytokine exposure was replicated experimentally through the use of a microfluidic platform that enabled the parallelized capture of dynamic single cell response to precisely delivered pulses of IL-2 stimulus. The in vitro results demonstrate that single cell response profiles vary with pulsatile IL-2 input at pre-equilibrium levels. These observations confirmed our model predictions that Jurkat cells have a preferred range of extracellular IL-2 fluctuations, in which downstream response is rapidly initiated. Further investigation into this filtering behavior could increase our understanding of how pre-existing cellular states within immune cell populations enable a systems response within a preferred range of ligand fluctuations, and whether the observed cytokine range corresponds to in vivo conditions.
Collapse
Affiliation(s)
- Linda E. Kippner
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America
| | - Melissa L. Kemp
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America
- * E-mail:
| |
Collapse
|
37
|
Vardi N, Chaturvedi S, Weinberger LS. Feedback-mediated signal conversion promotes viral fitness. Proc Natl Acad Sci U S A 2018; 115:E8803-E8810. [PMID: 30150412 PMCID: PMC6140503 DOI: 10.1073/pnas.1802905115] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A fundamental signal-processing problem is how biological systems maintain phenotypic states (i.e., canalization) long after degradation of initial catalyst signals. For example, to efficiently replicate, herpesviruses (e.g., human cytomegalovirus, HCMV) rapidly counteract cell-mediated silencing using transactivators packaged in the tegument of the infecting virion particle. However, the activity of these tegument transactivators is inherently transient-they undergo immediate proteolysis but delayed synthesis-and how transient activation sustains lytic viral gene expression despite cell-mediated silencing is unclear. By constructing a two-color, conditional-feedback HCMV mutant, we find that positive feedback in HCMV's immediate-early 1 (IE1) protein is of sufficient strength to sustain HCMV lytic expression. Single-cell time-lapse imaging and mathematical modeling show that IE1 positive feedback converts transient transactivation signals from tegument pp71 proteins into sustained lytic expression, which is obligate for efficient viral replication, whereas attenuating feedback decreases fitness by promoting a reversible silenced state. Together, these results identify a regulatory mechanism enabling herpesviruses to sustain expression despite transient activation signals-akin to early electronic transistors-and expose a potential target for therapeutic intervention.
Collapse
Affiliation(s)
- Noam Vardi
- Gladstone-University of California, San Francisco (UCSF) Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158
| | - Sonali Chaturvedi
- Gladstone-University of California, San Francisco (UCSF) Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158
| | - Leor S Weinberger
- Gladstone-University of California, San Francisco (UCSF) Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158;
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| |
Collapse
|
38
|
Inoue H, Kunida K, Matsuda N, Hoshino D, Wada T, Imamura H, Noji H, Kuroda S. Automatic Quantitative Segmentation of Myotubes Reveals Single-cell Dynamics of S6 Kinase Activation. Cell Struct Funct 2018; 43:153-169. [PMID: 30047513 DOI: 10.1247/csf.18012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Automatic cell segmentation is a powerful method for quantifying signaling dynamics at single-cell resolution in live cell fluorescence imaging. Segmentation methods for mononuclear and round shape cells have been developed extensively. However, a segmentation method for elongated polynuclear cells, such as differentiated C2C12 myotubes, has yet to be developed. In addition, myotubes are surrounded by undifferentiated reserve cells, making it difficult to identify background regions and subsequent quantification. Here we developed an automatic quantitative segmentation method for myotubes using watershed segmentation of summed binary images and a two-component Gaussian mixture model. We used time-lapse fluorescence images of differentiated C2C12 cells stably expressing Eevee-S6K, a fluorescence resonance energy transfer (FRET) biosensor of S6 kinase (S6K). Summation of binary images enhanced the contrast between myotubes and reserve cells, permitting detection of a myotube and a myotube center. Using a myotube center instead of a nucleus, individual myotubes could be detected automatically by watershed segmentation. In addition, a background correction using the two-component Gaussian mixture model permitted automatic signal intensity quantification in individual myotubes. Thus, we provide an automatic quantitative segmentation method by combining automatic myotube detection and background correction. Furthermore, this method allowed us to quantify S6K activity in individual myotubes, demonstrating that some of the temporal properties of S6K activity such as peak time and half-life of adaptation show different dose-dependent changes of insulin between cell population and individuals.Key words: time lapse images, cell segmentation, fluorescence resonance energy transfer, C2C12, myotube.
Collapse
Affiliation(s)
- Haruki Inoue
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo
| | - Katsuyuki Kunida
- Laboratory of Computational Biology, Graduate School of Biological Sciences, Nara Institute of Science and Technology.,Department of Biological Sciences, Graduate School of Science, University of Tokyo
| | - Naoki Matsuda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo
| | - Daisuke Hoshino
- Department of Biological Sciences, Graduate School of Science, University of Tokyo.,Department of Engineering Science, Graduate School of Informatics and Engineering, University of Electro-Communications
| | - Takumi Wada
- Department of Biological Sciences, Graduate School of Science, University of Tokyo
| | - Hiromi Imamura
- Department of Functional Biology, Graduate School of Biostudies, Kyoto University
| | - Hiroyuki Noji
- Department of Applied Chemistry, Graduate School of Engineering, University of Tokyo
| | - Shinya Kuroda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo.,Department of Biological Sciences, Graduate School of Science, University of Tokyo.,CREST, Japan Science and Technology Corporation
| |
Collapse
|
39
|
Gupta S, Hainsworth L, Hogg JS, Lee REC, Faeder JR. Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology. PROCEEDINGS. EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING 2018; 2018:690-697. [PMID: 30175326 DOI: 10.1109/pdp2018.2018.00114] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://github.com/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.
Collapse
Affiliation(s)
- Sanjana Gupta
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| | - Liam Hainsworth
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| | - Justin S Hogg
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| | - Robin E C Lee
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| | - James R Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| |
Collapse
|
40
|
Loos C, Moeller K, Fröhlich F, Hucho T, Hasenauer J. A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-To-Cell Variability. Cell Syst 2018; 6:593-603.e13. [DOI: 10.1016/j.cels.2018.04.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 09/28/2017] [Accepted: 04/10/2018] [Indexed: 12/23/2022]
|
41
|
Sáez PJ, Sáez JC, Lennon-Duménil AM, Vargas P. Role of calcium permeable channels in dendritic cell migration. Curr Opin Immunol 2018; 52:74-80. [PMID: 29715579 DOI: 10.1016/j.coi.2018.04.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 04/03/2018] [Accepted: 04/05/2018] [Indexed: 12/22/2022]
Abstract
Calcium ion (Ca2+) is an essential second messenger involved in multiple cellular and subcellular processes. Ca2+ can be released and sensed globally or locally within cells, providing complex signals of variable amplitudes and time-scales. The key function of Ca2+ in the regulation of acto-myosin contractility has provided a simple explanation for its role in the regulation of immune cell migration. However, many questions remain, including the identity of the Ca2+ stores, channels and upstream signals involved in this process. Here, we focus on dendritic cells (DCs), because their immune sentinel function heavily relies on their capacity to migrate within tissues and later on between tissues and lymphoid organs. Deciphering the mechanisms by which cytoplasmic Ca2+ regulate DC migration should shed light on their role in initiating and tuning immune responses.
Collapse
Affiliation(s)
- Pablo J Sáez
- Institut Curie, PSL Research University, CNRS, UMR144, F-75005, France; Instittut Pierre-Gilles de Gennes, PSL Research University, F-75005, France.
| | - Juan C Sáez
- Departamento de Fisiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica, Santiago 6513677, Chile; Instituto Milenio, Centro Interdisciplinario de Neurociencias de Valparaíso, Valparaíso 2360103, Chile
| | | | - Pablo Vargas
- Institut Curie, PSL Research University, CNRS, UMR144, F-75005, France; Instittut Pierre-Gilles de Gennes, PSL Research University, F-75005, France.
| |
Collapse
|
42
|
Strasen J, Sarma U, Jentsch M, Bohn S, Sheng C, Horbelt D, Knaus P, Legewie S, Loewer A. Cell-specific responses to the cytokine TGFβ are determined by variability in protein levels. Mol Syst Biol 2018; 14:e7733. [PMID: 29371237 PMCID: PMC5787704 DOI: 10.15252/msb.20177733] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
The cytokine TGFβ provides important information during embryonic development, adult tissue homeostasis, and regeneration. Alterations in the cellular response to TGFβ are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time-resolved measurements of pathway activation at the single-cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single-cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFβ is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock-out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity.
Collapse
Affiliation(s)
- Jette Strasen
- Berlin Institute for Medical Systems Biology, Max Delbrueck Center in the Helmholtz Association, Berlin, Germany
| | - Uddipan Sarma
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Marcel Jentsch
- Berlin Institute for Medical Systems Biology, Max Delbrueck Center in the Helmholtz Association, Berlin, Germany.,Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Stefan Bohn
- Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Caibin Sheng
- Berlin Institute for Medical Systems Biology, Max Delbrueck Center in the Helmholtz Association, Berlin, Germany.,Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Daniel Horbelt
- Institute for Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany
| | - Petra Knaus
- Institute for Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany
| | | | - Alexander Loewer
- Berlin Institute for Medical Systems Biology, Max Delbrueck Center in the Helmholtz Association, Berlin, Germany .,Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| |
Collapse
|
43
|
Abstract
The robustness of biological systems is often depicted as a key system-level emergent property that allows uniform phenotypes in fluctuating environments. Yet, analysis of single-cell signaling responses identified multiple examples of cellular responses with high degrees of heterogeneity. Here we discuss the implications of the observed lack of response accuracy in the context of new observations coming from single-cell approaches. Single-cell approaches provide a new way to measure the abundance of thousands of molecular species in a single-cell. Repeatedly, analysis of cell distributions identifies clusters within these distributions where cells can be grouped into specific cell states. If cells in a population occupy distinct cell states, the observed variable response could in fact be accurate for each cell conditioned on its own internal state. In this view, the observed lack of accuracy, i.e. response heterogeneity, could in fact be beneficial and a potentially regulated feature of cell state variability. Therefore, to truly determine whether the observed response heterogeneity is a desired property or a physical limitation, future analysis of signaling heterogeneity must take into account the internal states of cells in the population.
Collapse
|
44
|
Coller HA. Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017. Physiol Genomics 2017; 49:491-495. [PMID: 28802263 PMCID: PMC5625270 DOI: 10.1152/physiolgenomics.00049.2017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 08/04/2017] [Accepted: 08/07/2017] [Indexed: 11/22/2022] Open
Abstract
Emerging technologies for the analysis of genome-wide information in single cells have the potential to transform many fields of biology, including our understanding of cell states, the response of cells to external stimuli, mosaicism, and intratumor heterogeneity. At Experimental Biology 2017 in Chicago, Physiological Genomics hosted a symposium in which five leaders in the field of single cell genomics presented their recent research. The speakers discussed emerging methodologies in single cell analysis and critical issues for the analysis of single cell data. Also discussed were applications of single cell genomics to understanding the different types of cells within an organism or tissue and the basis for cell-to-cell variability in response to stimuli.
Collapse
Affiliation(s)
- Hilary A Coller
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles and Department of Biological Chemistry, David Geffen School of Medicine, Los Angeles, California
| |
Collapse
|
45
|
Narciso CE, Contento NM, Storey TJ, Hoelzle DJ, Zartman JJ. Release of Applied Mechanical Loading Stimulates Intercellular Calcium Waves in Drosophila Wing Discs. Biophys J 2017; 113:491-501. [PMID: 28746859 PMCID: PMC5529297 DOI: 10.1016/j.bpj.2017.05.051] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 05/15/2017] [Accepted: 05/17/2017] [Indexed: 01/14/2023] Open
Abstract
Mechanical forces are critical but poorly understood inputs for organogenesis and wound healing. Calcium ions (Ca2+) are critical second messengers in cells for integrating environmental and mechanical cues, but the regulation of Ca2+ signaling is poorly understood in developing epithelial tissues. Here we report a chip-based regulated environment for microorgans that enables systematic investigations of the crosstalk between an organ's mechanical stress environment and biochemical signaling under genetic and chemical perturbations. This method enabled us to define the essential conditions for generating organ-scale intercellular Ca2+ waves in Drosophila wing discs that are also observed in vivo during organ development. We discovered that mechanically induced intercellular Ca2+ waves require fly extract growth serum as a chemical stimulus. Using the chip-based regulated environment for microorgans, we demonstrate that not the initial application but instead the release of mechanical loading is sufficient, but not necessary, to initiate intercellular Ca2+ waves. The Ca2+ response depends on the prestress intercellular Ca2+ activity and not on the magnitude or duration of the mechanical stimulation applied. Mechanically induced intercellular Ca2+ waves rely on IP3R-mediated Ca2+-induced Ca2+ release and propagation through gap junctions. Thus, intercellular Ca2+ waves in developing epithelia may be a consequence of stress dissipation during organ growth.
Collapse
Affiliation(s)
- Cody E Narciso
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana
| | - Nicholas M Contento
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana
| | - Thomas J Storey
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana
| | - David J Hoelzle
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana.
| | - Jeremiah J Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana.
| |
Collapse
|
46
|
Abstract
The heterogeneity in mammalian cells signaling response is largely a result of pre‐existing cell‐to‐cell variability. It is unknown whether cell‐to‐cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single‐cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single‐cell level.
Collapse
Affiliation(s)
- Jason Yao
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA, USA
| | - Anna Pilko
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA, USA
| | - Roy Wollman
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA, USA
| |
Collapse
|