1
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Weng C, Yu F, Yang D, Poeschla M, Liggett LA, Jones MG, Qiu X, Wahlster L, Caulier A, Hussmann JA, Schnell A, Yost KE, Koblan LW, Martin-Rufino JD, Min J, Hammond A, Ssozi D, Bueno R, Mallidi H, Kreso A, Escabi J, Rideout WM, Jacks T, Hormoz S, van Galen P, Weissman JS, Sankaran VG. Deciphering cell states and genealogies of human haematopoiesis. Nature 2024; 627:389-398. [PMID: 38253266 PMCID: PMC10937407 DOI: 10.1038/s41586-024-07066-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/12/2024] [Indexed: 01/24/2024]
Abstract
The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived haematopoietic stem cells (HSCs)1. Perturbations to this process underlie diverse diseases, but the clonal contributions to human haematopoiesis and how this changes with age remain incompletely understood. Although recent insights have emerged from barcoding studies in model systems2-5, simultaneous detection of cell states and phylogenies from natural barcodes in humans remains challenging. Here we introduce an improved, single-cell lineage-tracing system based on deep detection of naturally occurring mitochondrial DNA mutations with simultaneous readout of transcriptional states and chromatin accessibility. We use this system to define the clonal architecture of HSCs and map the physiological state and output of clones. We uncover functional heterogeneity in HSC clones, which is stable over months and manifests as both differences in total HSC output and biases towards the production of different mature cell types. We also find that the diversity of HSC clones decreases markedly with age, leading to an oligoclonal structure with multiple distinct clonal expansions. Our study thus provides a clonally resolved and cell-state-aware atlas of human haematopoiesis at single-cell resolution, showing an unappreciated functional diversity of human HSC clones and, more broadly, paving the way for refined studies of clonal dynamics across a range of tissues in human health and disease.
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Affiliation(s)
- Chen Weng
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fulong Yu
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou, P.R. China
| | - Dian Yang
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Molecular Pharmacology and Therapeutics, Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Michael Poeschla
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - L Alexander Liggett
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew G Jones
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Dermatology, Stanford University, Stanford, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Genetics and Computer Science, BASE Research Initiative, Betty Irene Moore Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Lara Wahlster
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexis Caulier
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jeffrey A Hussmann
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alexandra Schnell
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kathryn E Yost
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Luke W Koblan
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jorge D Martin-Rufino
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph Min
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alessandro Hammond
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel Ssozi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Raphael Bueno
- Division of Thoracic and Cardiac Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Hari Mallidi
- Division of Thoracic and Cardiac Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Antonia Kreso
- Division of Cardiac Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Javier Escabi
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - William M Rideout
- Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA
| | - Tyler Jacks
- Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA
| | - Sahand Hormoz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Peter van Galen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
- Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA.
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
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2
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Sahay S, Adhikari S, Hormoz S, Chakrabarti S. An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells. Bioinformatics 2023; 39:btad602. [PMID: 37769241 PMCID: PMC10576164 DOI: 10.1093/bioinformatics/btad602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 09/30/2023]
Abstract
MOTIVATION Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms (for instance in gene expression, eclosion, egg-laying, and feeding) tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets, and are also limited by their use of P-values in detecting oscillations. RESULTS We introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process regression and Bayesian inference to incorporate measurement errors, non-uniformly sampled data, and a recently developed non-stationary kernel to improve detection of oscillations. By using Bayes factors, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses, thus providing an advantage over P-values. Using synthetic datasets, we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary symmetric oscillations. Next, by analyzing existing qPCR datasets, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak and noisy oscillations. Finally, we generate new qPCR data on mouse embryonic stem cells. Surprisingly, we discover using ODeGP that increasing cell-density results in rapid generation of oscillations in the Bmal1 gene, thus highlighting our method's ability to discover unexpected and new patterns. In its current implementation, ODeGP is meant only for analyzing single or a few time-trajectories, not genome-wide datasets. AVAILABILITY AND IMPLEMENTATION ODeGP is available at https://github.com/Shaonlab/ODeGP.
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Affiliation(s)
- Shabnam Sahay
- Department of Computer Science, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, Karnataka 560065, India
| | - Shishir Adhikari
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, United States
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, United States
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, United States
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, United States
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
| | - Shaon Chakrabarti
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, Karnataka 560065, India
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3
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Muyas F, Sauer CM, Valle-Inclán JE, Li R, Rahbari R, Mitchell TJ, Hormoz S, Cortés-Ciriano I. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat Biotechnol 2023:10.1038/s41587-023-01863-z. [PMID: 37414936 DOI: 10.1038/s41587-023-01863-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/07/2023] [Indexed: 07/08/2023]
Abstract
Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.
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Affiliation(s)
- Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Carolin M Sauer
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Jose Espejo Valle-Inclán
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Ruoyan Li
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Raheleh Rahbari
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Thomas J Mitchell
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
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4
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Sahay S, Adhikari S, Hormoz S, Chakrabarti S. An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells. bioRxiv 2023:2023.03.21.533651. [PMID: 36993318 PMCID: PMC10055182 DOI: 10.1101/2023.03.21.533651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method, ODeGP ( O scillation De tection using G aussian P rocesses), which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene, thus highlighting our method’s ability to discover unexpected patterns. In its current implementation, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.
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Affiliation(s)
- Shabnam Sahay
- Department of Computer Science, Indian Institute of Technology Bombay
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore
| | - Shishir Adhikari
- Department of Systems Biology, Harvard Medical School, Boston
- Department of Data Science, Dana-Farber Cancer Institute, Boston
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston
- Department of Data Science, Dana-Farber Cancer Institute, Boston
- Broad Institute of MIT and Harvard, Cambridge
| | - Shaon Chakrabarti
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore
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5
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McNulty R, Sritharan D, Pahng SH, Meisch JP, Liu S, Brennan MA, Saxer G, Hormoz S, Rosenthal AZ. Probe-based bacterial single-cell RNA sequencing predicts toxin regulation. Nat Microbiol 2023; 8:934-945. [PMID: 37012420 PMCID: PMC10159851 DOI: 10.1038/s41564-023-01348-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 02/25/2023] [Indexed: 04/05/2023]
Abstract
Clonal bacterial populations rely on transcriptional variation across individual cells to produce specialized states that increase fitness. Understanding all cell states requires studying isogenic bacterial populations at the single-cell level. Here we developed probe-based bacterial sequencing (ProBac-seq), a method that uses libraries of DNA probes and an existing commercial microfluidic platform to conduct bacterial single-cell RNA sequencing. We sequenced the transcriptome of thousands of individual bacterial cells per experiment, detecting several hundred transcripts per cell on average. Applied to Bacillus subtilis and Escherichia coli, ProBac-seq correctly identifies known cell states and uncovers previously unreported transcriptional heterogeneity. In the context of bacterial pathogenesis, application of the approach to Clostridium perfringens reveals heterogeneous expression of toxin by a subpopulation that can be controlled by acetate, a short-chain fatty acid highly prevalent in the gut. Overall, ProBac-seq can be used to uncover heterogeneity in isogenic microbial populations and identify perturbations that affect pathogenicity.
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Affiliation(s)
- Ryan McNulty
- IFF Health and Biosciences, Wilmington, DE, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Duluxan Sritharan
- Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Seong Ho Pahng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | | | - Shichen Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Gerda Saxer
- IFF Health and Biosciences, Wilmington, DE, USA
| | - Sahand Hormoz
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Adam Z Rosenthal
- IFF Health and Biosciences, Wilmington, DE, USA.
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA.
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6
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Sevier SA, Hormoz S. Collective polymerase dynamics emerge from DNA supercoiling during transcription. Biophys J 2022; 121:4153-4165. [PMID: 36171726 PMCID: PMC9675029 DOI: 10.1016/j.bpj.2022.09.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/19/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022] Open
Abstract
All biological processes ultimately come from physical interactions. The mechanical properties of DNA play a critical role in transcription. RNA polymerase can over or under twist DNA (referred to as DNA supercoiling) when it moves along a gene, resulting in mechanical stresses in DNA that impact its own motion and that of other polymerases. For example, when enough supercoiling accumulates, an isolated polymerase halts, and transcription stops. DNA supercoiling can also mediate nonlocal interactions between polymerases that shape gene expression fluctuations. Here, we construct a comprehensive model of transcription that captures how RNA polymerase motion changes the degree of DNA supercoiling, which in turn feeds back into the rate at which polymerases are recruited and move along the DNA. Surprisingly, our model predicts that a group of three or more polymerases move together at a constant velocity and sustain their motion (forming what we call a polymeton), whereas one or two polymerases would have halted. We further show that accounting for the impact of DNA supercoiling on both RNA polymerase recruitment and velocity recapitulates empirical observations of gene expression fluctuations. Finally, we propose a mechanical toggle switch whereby interactions between genes are mediated by DNA twisting as opposed to proteins. Understanding the mechanical regulation of gene expression provides new insights into how endogenous genes can interact and informs the design of new forms of engineered interactions.
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Affiliation(s)
- Stuart A Sevier
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts; Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
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7
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Budjan C, Liu S, Ranga A, Gayen S, Pourquié O, Hormoz S. Paraxial mesoderm organoids model development of human somites. eLife 2022; 11:68925. [PMID: 35088712 PMCID: PMC8906808 DOI: 10.7554/elife.68925] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 01/27/2022] [Indexed: 11/23/2022] Open
Abstract
During the development of the vertebrate embryo, segmented structures called somites are periodically formed from the presomitic mesoderm (PSM) and give rise to the vertebral column. While somite formation has been studied in several animal models, it is less clear how well this process is conserved in humans. Recent progress has made it possible to study aspects of human paraxial mesoderm (PM) development such as the human segmentation clock in vitro using human pluripotent stem cells (hPSCs); however, somite formation has not been observed in these monolayer cultures. Here, we describe the generation of human PM organoids from hPSCs (termed Somitoids), which recapitulate the molecular, morphological, and functional features of PM development, including formation of somite-like structures in vitro. Using a quantitative image-based screen, we identify critical parameters such as initial cell number and signaling modulations that reproducibly yielded formation of somite-like structures in our organoid system. In addition, using single-cell RNA-sequencing and 3D imaging, we show that PM organoids both transcriptionally and morphologically resemble their in vivo counterparts and can be differentiated into somite derivatives. Our organoid system is reproducible and scalable, allowing for the systematic and quantitative analysis of human spine development and disease in vitro. Humans are part of a group of animals called vertebrates, which are all the animals with backbones. Broadly, all vertebrates have a similar body shape with a head at one end and a left and right side that are similar to each other. Although this is not very obvious in humans, vertebrate bodies are derived from pairs of segments arranged from the head to the tail. Each of these segments or somites originates early in embryonic development. Cells from each somite then divide, grow and specialize to form bones such as the vertebrae of the vertebral column, muscles, skin, and other tissues that make up each segment. Studying different animals during embryonic development has provided insights into how somites form and grow, but it is technically difficult to do and only provides an approximate model of how somites develop in humans. Being able to make and study somites using human cells in the lab would help scientists learn more about how somite formation in humans is regulated. Budjan et al. grew human stem cells in the lab as three-dimensional structures called organoids, and used chemical signals similar to the ones produced in the embryo during development to make the cells form somites. Various combinations of signals were tested to find the best way to trigger somite formation. Once the somites formed, Budjan et al. measured them and studied their structure and the genes they used. They found that these lab-grown somites have the same size and structure as natural somites and use many of the same genes. This new organoid model provides a way to study human somite formation and development in the lab for the first time. This can provide insights into the development and evolution of humans and other animals that could then help scientists understand diseases such as the development of abnormal spinal curvature that affects around 1 in 10,000 newborns.
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Affiliation(s)
- Christoph Budjan
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States
| | - Shichen Liu
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States
| | - Adrian Ranga
- KU Leuven, KU Leuven, Department of Mechanical Engineering, Belgium
| | - Senjuti Gayen
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States
| | - Olivier Pourquié
- Department of Genetics, Harvard Medical School, Boston, United States
| | - Sahand Hormoz
- Department of Genetics, Dana-Farber Cancer Institute, Boston, United States
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8
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Wang A, Zhang Q, Han Y, Megason S, Hormoz S, Mosaliganti KR, Lam JCK, Li VOK. A novel deep learning-based 3D cell segmentation framework for future image-based disease detection. Sci Rep 2022; 12:342. [PMID: 35013443 PMCID: PMC8748745 DOI: 10.1038/s41598-021-04048-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 12/09/2021] [Indexed: 11/12/2022] Open
Abstract
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets. We develop a deep learning-based 3D cell segmentation pipeline, 3DCellSeg, to address these challenges. Compared to the existing methods, our approach carries the following novelties: (1) a robust two-stage pipeline, requiring only one hyperparameter; (2) a light-weight deep convolutional neural network (3DCellSegNet) to efficiently output voxel-wise masks; (3) a custom loss function (3DCellSeg Loss) to tackle the clumped cell problem; and (4) an efficient touching area-based clustering algorithm (TASCAN) to separate 3D cells from the foreground masks. Cell segmentation experiments conducted on four different cell datasets show that 3DCellSeg outperforms the baseline models on the ATAS (plant), HMS (animal), and LRP (plant) datasets with an overall accuracy of 95.6%, 76.4%, and 74.7%, respectively, while achieving an accuracy comparable to the baselines on the Ovules (plant) dataset with an overall accuracy of 82.2%. Ablation studies show that the individual improvements in accuracy is attributable to 3DCellSegNet, 3DCellSeg Loss, and TASCAN, with the 3DCellSeg demonstrating robustness across different datasets and cell shapes. Our results suggest that 3DCellSeg can serve a powerful biomedical and clinical tool, such as histo-pathological image analysis, for cancer diagnosis and grading.
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Affiliation(s)
- Andong Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Qi Zhang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Yang Han
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Sean Megason
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | | | - Jacqueline C K Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
| | - Victor O K Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
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9
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Sritharan D, Wang S, Hormoz S. Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry. Proc Natl Acad Sci U S A 2021; 118:e2100473118. [PMID: 34272279 PMCID: PMC8307776 DOI: 10.1073/pnas.2100473118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Most high-dimensional datasets are thought to be inherently low-dimensional-that is, data points are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here, we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifold's embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. We found that the intrinsic approach fails to accurately estimate the curvature of even a two-dimensional constant-curvature manifold, whereas the extrinsic approach was able to handle more complex toy models, even when confounded by practical constraints like small sample sizes and measurement noise. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches and recapitulated its topological classification as a Klein bottle. Lastly, we applied the extrinsic approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells to quantify the Riemannian curvature of scRNAseq manifolds.
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Affiliation(s)
- Duluxan Sritharan
- Harvard Graduate Program in Biophysics, Harvard University, Boston, MA 02115
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215
| | - Shu Wang
- Harvard Graduate Program in Biophysics, Harvard University, Boston, MA 02115
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115
| | - Sahand Hormoz
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215;
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
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10
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Liu S, Nguyen M, Hormoz S. Integrating readout of somatic mutations in individual cells with single-cell transcriptional profiling. STAR Protoc 2021; 2:100673. [PMID: 34337442 PMCID: PMC8313752 DOI: 10.1016/j.xpro.2021.100673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In many biological applications, the readout of somatic mutations in individual cells is essential. For example, it can be used to mark individual cancer cells or identify progenies of a stem cell. Here, we present a protocol to perform single-cell RNA-seq and single-cell amplicon-seq using 10X Chromium technology. Our protocol demonstrates how to (1) isolate CD34+ progenitor cells from human bone marrow aspirate, (2) prepare single-cell amplicon libraries, and (3) analyze the libraries to assign somatic mutations to individual cells. For complete details on the use and execution of this protocol, please refer to Van Egeren et al. (2021). Isolation of CD34+ cells from human bone marrow aspirates Enrichment of target somatic mutations from single-cell cDNA Protocol enables single-cell RNA sequencing alongside single-cell amplicon sequencing
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Affiliation(s)
- Shichen Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Maximilian Nguyen
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Sahand Hormoz
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
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11
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Van Egeren D, Escabi J, Nguyen M, Liu S, Reilly CR, Patel S, Kamaz B, Kalyva M, DeAngelo DJ, Galinsky I, Wadleigh M, Winer ES, Luskin MR, Stone RM, Garcia JS, Hobbs GS, Camargo FD, Michor F, Mullally A, Cortes-Ciriano I, Hormoz S. Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms. Cell Stem Cell 2021; 28:514-523.e9. [PMID: 33621486 PMCID: PMC7939520 DOI: 10.1016/j.stem.2021.02.001] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/01/2020] [Accepted: 01/28/2021] [Indexed: 12/11/2022]
Abstract
Some cancers originate from a single mutation event in a single cell. Blood cancers known as myeloproliferative neoplasms (MPNs) are thought to originate when a driver mutation is acquired by a hematopoietic stem cell (HSC). However, when the mutation first occurs in individuals and how it affects the behavior of HSCs in their native context is not known. Here we quantified the effect of the JAK2-V617F mutation on the self-renewal and differentiation dynamics of HSCs in treatment-naive individuals with MPNs and reconstructed lineage histories of individual HSCs using somatic mutation patterns. We found that JAK2-V617F mutations occurred in a single HSC several decades before MPN diagnosis-at age 9 ± 2 years in a 34-year-old individual and at age 19 ± 3 years in a 63-year-old individual-and found that mutant HSCs have a selective advantage in both individuals. These results highlight the potential of harnessing somatic mutations to reconstruct cancer lineages.
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Affiliation(s)
- Debra Van Egeren
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Javier Escabi
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Research Scholar Initiative, Harvard Graduate School of Arts and Sciences, Cambridge, MA 02138, USA
| | - Maximilian Nguyen
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Shichen Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Christopher R Reilly
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Sachin Patel
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Baransel Kamaz
- Division of Hematology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Maria Kalyva
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Daniel J DeAngelo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Ilene Galinsky
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Martha Wadleigh
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Eric S Winer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Marlise R Luskin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Richard M Stone
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jacqueline S Garcia
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Gabriela S Hobbs
- Leukemia Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Fernando D Camargo
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; The Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02115, USA; The Ludwig Center at Harvard, Boston, MA 02115, USA
| | - Ann Mullally
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Division of Hematology, Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Isidro Cortes-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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12
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Hormoz S. Evaluation of Arai et al.: What Can Differences across Daughter Cells Tell Us about Stem Cell Renewal? Cell Syst 2020; 11:547-549. [PMID: 33333027 DOI: 10.1016/j.cels.2020.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
One snapshot of the peer review process for "Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal" (Arai et al., 2020).
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Affiliation(s)
- Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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13
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Bowling S, Sritharan D, Osorio FG, Nguyen M, Cheung P, Rodriguez-Fraticelli A, Patel S, Yuan WC, Fujiwara Y, Li BE, Orkin SH, Hormoz S, Camargo FD. An Engineered CRISPR-Cas9 Mouse Line for Simultaneous Readout of Lineage Histories and Gene Expression Profiles in Single Cells. Cell 2020; 181:1410-1422.e27. [PMID: 32413320 PMCID: PMC7529102 DOI: 10.1016/j.cell.2020.04.048] [Citation(s) in RCA: 130] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 02/20/2020] [Accepted: 04/24/2020] [Indexed: 12/29/2022]
Abstract
Tracing the lineage history of cells is key to answering diverse and fundamental questions in biology. Coupling of cell ancestry information with other molecular readouts represents an important goal in the field. Here, we describe the CRISPR array repair lineage tracing (CARLIN) mouse line and corresponding analysis tools that can be used to simultaneously interrogate the lineage and transcriptomic information of single cells in vivo. This model exploits CRISPR technology to generate up to 44,000 transcribed barcodes in an inducible fashion at any point during development or adulthood, is compatible with sequential barcoding, and is fully genetically defined. We have used CARLIN to identify intrinsic biases in the activity of fetal liver hematopoietic stem cell (HSC) clones and to uncover a previously unappreciated clonal bottleneck in the response of HSCs to injury. CARLIN also allows the unbiased identification of transcriptional signatures associated with HSC activity without cell sorting.
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Affiliation(s)
- Sarah Bowling
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Duluxan Sritharan
- Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Fernando G Osorio
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Maximilian Nguyen
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Priscilla Cheung
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Alejo Rodriguez-Fraticelli
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sachin Patel
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Wei-Chien Yuan
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Yuko Fujiwara
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bin E Li
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stuart H Orkin
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Boston, MA, USA
| | - Sahand Hormoz
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Fernando D Camargo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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14
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Bowling S, Sritharan D, Osorio FG, Nguyen M, Cheung P, Rodriguez-Fraticelli A, Patel S, Yuan WC, Fujiwara Y, Li BE, Orkin SH, Hormoz S, Camargo FD. An Engineered CRISPR-Cas9 Mouse Line for Simultaneous Readout of Lineage Histories and Gene Expression Profiles in Single Cells. Cell 2020; 181:1693-1694. [PMID: 32589959 DOI: 10.1016/j.cell.2020.06.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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15
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Hormoz S, Singer ZS, Linton JM, Antebi YE, Shraiman BI, Elowitz MB. Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements. Cell Syst 2019; 3:419-433.e8. [PMID: 27883889 DOI: 10.1016/j.cels.2016.10.015] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 06/01/2016] [Accepted: 10/18/2016] [Indexed: 12/28/2022]
Abstract
As they proliferate, living cells undergo transitions between specific molecularly and developmentally distinct states. Despite the functional centrality of these transitions in multicellular organisms, it has remained challenging to determine which transitions occur and at what rates without perturbations and cell engineering. Here, we introduce kin correlation analysis (KCA) and show that quantitative cell-state transition dynamics can be inferred, without direct observation, from the clustering of cell states on pedigrees (lineage trees). Combining KCA with pedigrees obtained from time-lapse imaging and endpoint single-molecule RNA-fluorescence in situ hybridization (RNA-FISH) measurements of gene expression, we determined the cell-state transition network of mouse embryonic stem (ES) cells. This analysis revealed that mouse ES cells exhibit stochastic and reversible transitions along a linear chain of states ranging from 2C-like to epiblast-like. Our approach is broadly applicable and may be applied to systems with irreversible transitions and non-stationary dynamics, such as in cancer and development.
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Affiliation(s)
- Sahand Hormoz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Zakary S Singer
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - James M Linton
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Yaron E Antebi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Boris I Shraiman
- Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute (HHMI) and Department of Applied Physics, California Institute of Technology, Pasadena, CA 91125, USA.
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16
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Rosenthal AZ, Qi Y, Hormoz S, Park J, Li SHJ, Elowitz MB. Metabolic interactions between dynamic bacterial subpopulations. eLife 2018; 7:33099. [PMID: 29809139 PMCID: PMC6025961 DOI: 10.7554/elife.33099] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 05/21/2018] [Indexed: 01/08/2023] Open
Abstract
Individual microbial species are known to occupy distinct metabolic niches within multi-species communities. However, it has remained largely unclear whether metabolic specialization can similarly occur within a clonal bacterial population. More specifically, it is not clear what functions such specialization could provide and how specialization could be coordinated dynamically. Here, we show that exponentially growing Bacillus subtilis cultures divide into distinct interacting metabolic subpopulations, including one population that produces acetate, and another population that differentially expresses metabolic genes for the production of acetoin, a pH-neutral storage molecule. These subpopulations exhibit distinct growth rates and dynamic interconversion between states. Furthermore, acetate concentration influences the relative sizes of the different subpopulations. These results show that clonal populations can use metabolic specialization to control the environment through a process of dynamic, environmentally-sensitive state-switching. The chemical reactions that occur within a living organism are collectively referred to as its metabolism. Many metabolic reactions produce byproducts that will poison the cells if they are not dealt with: fermenting bacteria, for example, release harmful organic acids and alcohols. How the bacteria respond to these toxins has been most studied at the level of entire microbial populations, meaning the activities of individual cells are effectively “averaged” together. Yet, even two bacteria with the same genes and living in the same environment can behave in different ways. This raises the question: do bacterial populations specialize into distinct subpopulations that play distinct roles when dealing with metabolic products, or do all cells in the community act in unison? Rosenthal et al. set out to answer this question for a community of Bacillus subtilis, a bacterium that is commonly studied in the laboratory and used for the industrial production of enzymes. The analysis focused on genes involved in fundamental metabolic processes, known as the TCA cycle, which the bacteria use to generate energy and build biomass. The experiments revealed that, even when all the cells are genetically identical, different Bacillus subtilis cells do indeed specialize into metabolic subpopulations with distinct growth rates. Time-lapse movies of bacteria that made fluorescent markers of different colors whenever certain metabolic genes became active showed cells switching different colors on and off, indicating that they switch between metabolic subpopulations. Further biochemical studies and measures of gene activity revealed that the different subpopulations produce and release distinct metabolic products, including toxic byproducts. Notably, the release of these metabolites by one subpopulation appeared to activate other subpopulations within the community. This example of cells specializing into unique interacting metabolic subpopulations provides insight into several fundamental issues in microbiology and beyond. It is relevant to evolutionary biologists, since the fact that fractions of the population can switch in and out of a metabolic state, instead of evolving into several inflexible specialists, may provide an evolutionary advantage in fluctuating natural environments by reducing the risk of extinction. It also has implications for industrial fermentation processes and metabolic engineering, and may help biotechnologists design more efficient ways to harness bacterial metabolism to produce useful products.
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Affiliation(s)
- Adam Z Rosenthal
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States.,Department of Applied Physics, California Institute of Technology, Pasadena, United States
| | - Yutao Qi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States.,Department of Applied Physics, California Institute of Technology, Pasadena, United States
| | - Sahand Hormoz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States.,Department of Applied Physics, California Institute of Technology, Pasadena, United States
| | - Jin Park
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States.,Department of Applied Physics, California Institute of Technology, Pasadena, United States
| | - Sophia Hsin-Jung Li
- Department of Molecular Biology, Princeton University, Princeton, United States
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States.,Department of Applied Physics, California Institute of Technology, Pasadena, United States.,Howard Hughes Medical Institute, Pasadena, United States
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17
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Park J, Dies M, Lin Y, Hormoz S, Smith-Unna SE, Quinodoz S, Hernández-Jiménez MJ, Garcia-Ojalvo J, Locke JCW, Elowitz MB. Molecular Time Sharing through Dynamic Pulsing in Single Cells. Cell Syst 2018; 6:216-229.e15. [PMID: 29454936 PMCID: PMC6070344 DOI: 10.1016/j.cels.2018.01.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 09/04/2017] [Accepted: 01/10/2018] [Indexed: 11/19/2022]
Abstract
In cells, specific regulators often compete for limited amounts of a core enzymatic resource. It is typically assumed that competition leads to partitioning of core enzyme molecules among regulators at constant levels. Alternatively, however, different regulatory species could time share, or take turns utilizing, the core resource. Using quantitative time-lapse microscopy, we analyzed sigma factor activity dynamics, and their competition for RNA polymerase, in individual Bacillus subtilis cells under energy stress. Multiple alternative sigma factors were activated in ~1-hr pulses in stochastic and repetitive fashion. Pairwise analysis revealed that two sigma factors rarely pulse simultaneously and that some pairs are anti-correlated, indicating that RNAP utilization alternates among different sigma factors. Mathematical modeling revealed how stochastic time-sharing dynamics can emerge from pulse-generating sigma factor regulatory circuits actively competing for RNAP. Time sharing provides a mechanism for cells to dynamically control the distribution of cell states within a population. Since core molecular components are limiting in many other systems, time sharing may represent a general mode of regulation.
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Affiliation(s)
- Jin Park
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Marta Dies
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, 08003 Barcelona, Spain; Department of Physics and Nuclear Engineering, Universitat Politecnica de Catalunya, 08222 Terrassa, Spain; Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Yihan Lin
- Center for Quantitative Biology, and Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Sahand Hormoz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Sofia Quinodoz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Jordi Garcia-Ojalvo
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, 08003 Barcelona, Spain.
| | - James C W Locke
- Sainsbury Laboratory, Cambridge University, Bateman Street, Cambridge CB2 1LR, UK; Microsoft Research, Cambridge, UK.
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA.
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18
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Frieda KL, Linton JM, Hormoz S, Choi J, Chow KHK, Singer ZS, Budde MW, Elowitz MB, Cai L. Synthetic recording and in situ readout of lineage information in single cells. Nature 2017; 541:107-111. [PMID: 27869821 PMCID: PMC6487260 DOI: 10.1038/nature20777] [Citation(s) in RCA: 263] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 11/11/2016] [Indexed: 12/13/2022]
Abstract
Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here we describe a synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out of single cells in situ. This system, termed memory by engineered mutagenesis with optical in situ readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The state of a given scratchpad can be irreversibly altered by CRISPR/Cas9-based targeted mutagenesis, and later read out in single cells through multiplexed single-molecule RNA fluorescence hybridization (smFISH). Using MEMOIR as a proof of principle, we engineered mouse embryonic stem cells to contain multiple scratchpads and other recording components. In these cells, scratchpads were altered in a progressive and stochastic fashion as the cells proliferated. Analysis of the final states of scratchpads in single cells in situ enabled reconstruction of lineage information from cell colonies. Combining analysis of endogenous gene expression with lineage reconstruction in the same cells further allowed inference of the dynamic rates at which embryonic stem cells switch between two gene expression states. Finally, using simulations, we show how parallel MEMOIR systems operating in the same cell could enable recording and readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for information recording and in situ, single-cell readout across diverse biological systems.
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Affiliation(s)
- Kirsten L Frieda
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - James M Linton
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Sahand Hormoz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Joonhyuk Choi
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Ke-Huan K Chow
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Zakary S Singer
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Mark W Budde
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
- Howard Hughes Medical Institute, California Institute of Technology, Pasadena, California 91125, USA
| | - Long Cai
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
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19
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Hormoz S, Bhanot G, Biehl M, Bilal E, Meyer P, Norel R, Rhrissorrakrai K, Dayarian A. Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets. Bioinformatics 2014; 31:492-500. [PMID: 25152231 PMCID: PMC4325538 DOI: 10.1093/bioinformatics/btu569] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Motivation: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development. However, in many cases, a naive ‘extrapolation’ between the two species has not succeeded. As a result, clinical trials of new drugs sometimes fail even after considerable success in the mouse or rat stage of development. In addition to in vitro studies, inter-species translation requires analytical tools that can predict the enriched gene sets in human cells under various stimuli from corresponding measurements in animals. Such tools can improve our understanding of the underlying biology and optimize the allocation of resources for drug development. Results: We developed an algorithm to predict differential gene set enrichment as part of the sbv IMPROVER (systems biology verification in Industrial Methodology for Process Verification in Research) Species Translation Challenge, which focused on phosphoproteomic and transcriptomic measurements of normal human bronchial epithelial (NHBE) primary cells under various stimuli and corresponding measurements in rat (NRBE) primary cells. We find that gene sets exhibit a higher inter-species correlation compared with individual genes, and are potentially more suited for direct prediction. Furthermore, in contrast to a similar cross-species response in protein phosphorylation states 5 and 25 min after exposure to stimuli, gene set enrichment 6 h after exposure is significantly different in NHBE cells compared with NRBE cells. In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods. Availability and implementation: Implementation of all algorithms is available as source code (in Matlab) at http://bhanot.biomaps.rutgers.edu/wiki/codes_SC3_Predicting_GeneSets.zip, along with the relevant data used in the analysis. Gene sets, gene expression and protein phosphorylation data are available on request. Contact:hormoz@kitp.ucsb.edu
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Affiliation(s)
- Sahand Hormoz
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
| | - Gyan Bhanot
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
| | - Michael Biehl
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
| | - Erhan Bilal
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
| | - Pablo Meyer
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
| | - Raquel Norel
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
| | - Kahn Rhrissorrakrai
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
| | - Adel Dayarian
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA
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Dayarian A, Romero R, Wang Z, Biehl M, Bilal E, Hormoz S, Meyer P, Norel R, Rhrissorrakrai K, Bhanot G, Luo F, Tarca AL. Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. ACTA ACUST UNITED AC 2014; 31:462-70. [PMID: 25061067 DOI: 10.1093/bioinformatics/btu490] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue. RESULTS Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins. AVAILABILITY AND IMPLEMENTATION Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively. CONTACT gyanbhanot@gmail.com or luofeng@clemson.edu or atarca@med.wayne.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Adel Dayarian
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Roberto Romero
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Zhiming Wang
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Michael Biehl
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Erhan Bilal
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Sahand Hormoz
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Pablo Meyer
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Raquel Norel
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Kahn Rhrissorrakrai
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Gyan Bhanot
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Feng Luo
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
| | - Adi L Tarca
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, and Detroit, MI 48201, USA, College of Plant Protection and College of Science, Hunan Agricultural University, Changsha, 410128, China, School of Computing, Clemson University, Clemson, SC 29634, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, Department of Molecular Biology and Biochemistry, Department of Physics and BioMaPS Institute, Rutgers University, Piscataway, NJ 08854 and Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
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Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S. Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. Bioinformatics 2014; 31:453-61. [PMID: 24994890 PMCID: PMC4325536 DOI: 10.1093/bioinformatics/btu407] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. RESULTS Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. AVAILABILITY AND IMPLEMENTATION A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. CONTACT meikelbiehl@gmail.com.
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Affiliation(s)
- Michael Biehl
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Peter Sadowski
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Gyan Bhanot
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Erhan Bilal
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Adel Dayarian
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Pablo Meyer
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Raquel Norel
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Kahn Rhrissorrakrai
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Michael D Zeller
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Sahand Hormoz
- Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
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Fonda E, Meichle DP, Ouellette NT, Hormoz S, Lathrop DP. Direct observation of Kelvin waves excited by quantized vortex reconnection. Proc Natl Acad Sci U S A 2014; 111 Suppl 1:4707-10. [PMID: 24704878 PMCID: PMC3970858 DOI: 10.1073/pnas.1312536110] [Citation(s) in RCA: 115] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Quantized vortices are key features of quantum fluids such as superfluid helium and Bose-Einstein condensates. The reconnection of quantized vortices and subsequent emission of Kelvin waves along the vortices are thought to be central to dissipation in such systems. By visualizing the motion of submicron particles dispersed in superfluid (4)He, we have directly observed the emission of Kelvin waves from quantized vortex reconnection. We characterize one event in detail, using dimensionless similarity coordinates, and compare it with several theories. Finally, we give evidence for other examples of wavelike behavior in our system.
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Affiliation(s)
- Enrico Fonda
- Institute for Research in Electronics and Applied Physics and
- Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, 34127 Trieste, Italy
- Department of Physics, New York University, New York, NY 10003
| | - David P. Meichle
- Institute for Research in Electronics and Applied Physics and
- Department of Physics, University of Maryland, College Park, MD 20742
| | - Nicholas T. Ouellette
- Institute for Research in Electronics and Applied Physics and
- Department of Mechanical Engineering and Materials Science, Yale University, New Haven, CT 06520; and
| | - Sahand Hormoz
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106
| | - Daniel P. Lathrop
- Institute for Research in Electronics and Applied Physics and
- Department of Physics, University of Maryland, College Park, MD 20742
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Hormoz S. Cross talk and interference enhance information capacity of a signaling pathway. Biophys J 2013; 104:1170-80. [PMID: 23473500 DOI: 10.1016/j.bpj.2013.01.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2012] [Revised: 01/15/2013] [Accepted: 01/22/2013] [Indexed: 11/18/2022] Open
Abstract
A recurring motif in gene regulatory networks is transcription factors (TFs) that regulate each other and then bind to overlapping sites on DNA, where they interact and synergistically control transcription of a target gene. Here, we suggest that this motif maximizes information flow in a noisy network. Gene expression is an inherently noisy process due to thermal fluctuations and the small number of molecules involved. A consequence of multiple TFs interacting at overlapping binding sites is that their binding noise becomes correlated. Using concepts from information theory, we show that in general a signaling pathway transmits more information if 1), noise of one input is correlated with that of the other; and 2), input signals are not chosen independently. In the case of TFs, the latter criterion hints at upstream cross-regulation. We demonstrate these ideas for competing TFs and feed-forward gene-regulatory modules, and discuss generalizations to other signaling pathways. Our results challenge the conventional approach of treating biological noise as uncorrelated fluctuations, and present a systematic method for understanding TF cross-regulation networks either from direct measurements of binding noise or from bioinformatic analysis of overlapping binding sites.
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Affiliation(s)
- Sahand Hormoz
- Kavli Institute for Theoretical Physics, University of California-Santa Barbara, Santa Barbara, California, USA.
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Abstract
Cycling tissues such as the intestinal epithelium, germ line, and hair follicles, require a constant flux of differentiated cells. These tissues are maintained by a population of stem cells, which generate differentiated progenies and self-renew. Asymmetric division of each stem cell into one stem cell and one differentiated cell can accomplish both tasks. However, in mammalian cycling tissues, some stem cells divide symmetrically into two differentiated cells and are replaced by a neighbor that divides symmetrically into two stem cells. Besides this heterogeneity in fate (population asymmetry), stem cells also exhibit heterogenous proliferation-rates; in the long run, however, all stem cells proliferate at the same average rate (equipotency). We construct and simulate a mathematical model based on these experimental observations. We show that the complex steady-state dynamics of population-asymmetric stem cells reduces the rate of replicative aging of the tissue-potentially lowering the incidence of somatic mutations and genetics diseases such as cancer. Essentially, slow-dividing stem cells proliferate and purge the population of the fast-dividing - older - cells which had undertaken the majority of the tissue-generation burden. As the number of slow-dividing cells grows, their cycling-rate increases, eventually turning them into fast-dividers, which are themselves replaced by newly emerging slow-dividers. Going beyond current experiments, we propose a mechanism for equipotency that can potentially halve the rate of replicative aging. Our results highlight the importance of a population-level understanding of stem cells, and may explain the prevalence of population asymmetry in a wide variety of cycling tissues.
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Affiliation(s)
- Sahand Hormoz
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA.
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Hormoz S. Quantum collapse and the second law of thermodynamics. Phys Rev E Stat Nonlin Soft Matter Phys 2013; 87:022129. [PMID: 23496481 DOI: 10.1103/physreve.87.022129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Revised: 11/01/2012] [Indexed: 06/01/2023]
Abstract
A heat engine undergoes a cyclic operation while in equilibrium with the net result of conversion of heat into work. Quantum effects such as superposition of states can improve an engine's efficiency by breaking detailed balance, but this improvement comes at a cost due to excess entropy generated from collapse of superpositions on measurement. We quantify these competing facets for a quantum ratchet composed of an ensemble of pairs of interacting two-level atoms. We suggest that the measurement postulate of quantum mechanics is intricately connected to the second law of thermodynamics. More precisely, if quantum collapse is not inherently random, then the second law of thermodynamics can be violated. Our results challenge the conventional approach of simply quantifying quantum correlations as a thermodynamic work deficit.
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Affiliation(s)
- Sahand Hormoz
- Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, California 93106, USA.
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van Nierop EA, Hormoz S, House KZ, Aziz MJ. Effect of absorption enthalpy on temperature-swing CO2 separation process performance. ACTA ACUST UNITED AC 2011. [DOI: 10.1016/j.egypro.2011.02.054] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Belkin MA, Fan JA, Hormoz S, Capasso F, Khanna SP, Lachab M, Davies AG, Linfield EH. Terahertz quantum cascade lasers with copper metal-metal waveguides operating up to 178 K. Opt Express 2008; 16:3242-3248. [PMID: 18542411 DOI: 10.1364/oe.16.003242] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We report terahertz quantum cascade lasers operating in pulsed mode at an emission frequency of 3 THz and up to a maximum temperature of 178 K. The improvement in the maximum operating temperature is achieved by using a three-quantum-well active region design with resonant-phonon depopulation and by utilizing copper, instead of gold, for the cladding material in the metal-metal waveguides.
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Affiliation(s)
- Mikhail A Belkin
- Harvard School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
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