1
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McCutcheon SR, Rohm D, Iglesias N, Gersbach CA. Epigenome editing technologies for discovery and medicine. Nat Biotechnol 2024:10.1038/s41587-024-02320-1. [PMID: 39075148 DOI: 10.1038/s41587-024-02320-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 06/19/2024] [Indexed: 07/31/2024]
Abstract
Epigenome editing has rapidly evolved in recent years, with diverse applications that include elucidating gene regulation mechanisms, annotating coding and noncoding genome functions and programming cell state and lineage specification. Importantly, given the ubiquitous role of epigenetics in complex phenotypes, epigenome editing has unique potential to impact a broad spectrum of diseases. By leveraging powerful DNA-targeting technologies, such as CRISPR, epigenome editing exploits the heritable and reversible mechanisms of epigenetics to alter gene expression without introducing DNA breaks, inducing DNA damage or relying on DNA repair pathways.
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Affiliation(s)
- Sean R McCutcheon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Dahlia Rohm
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Nahid Iglesias
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Charles A Gersbach
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA.
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2
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Zhou H, Ye P, Xiong W, Duan X, Jing S, He Y, Zeng Z, Wei Y, Ye Q. Genome-scale CRISPR-Cas9 screening in stem cells: theories, applications and challenges. Stem Cell Res Ther 2024; 15:218. [PMID: 39026343 PMCID: PMC11264826 DOI: 10.1186/s13287-024-03831-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Due to the rapid development of stem cell technology, there have been tremendous advances in molecular biological and pathological research, cell therapy as well as organoid technologies over the past decades. Advances in genome editing technology, particularly the discovery of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-related protein 9 (Cas9), have further facilitated the rapid development of stem cell researches. The CRISPR-Cas9 technology now goes beyond creating single gene editing to enable the inhibition or activation of endogenous gene loci by fusing inhibitory (CRISPRi) or activating (CRISPRa) domains with deactivated Cas9 proteins (dCas9). These tools have been utilized in genome-scale CRISPRi/a screen to recognize hereditary modifiers that are synergistic or opposing to malady mutations in an orderly and fair manner, thereby identifying illness mechanisms and discovering novel restorative targets to accelerate medicinal discovery investigation. However, the application of this technique is still relatively rare in stem cell research. There are numerous specialized challenges in applying large-scale useful genomics approaches to differentiated stem cell populations. Here, we present the first comprehensive review on CRISPR-based functional genomics screening in the field of stem cells, as well as practical considerations implemented in a range of scenarios, and exploration of the insights of CRISPR-based screen into cell fates, disease mechanisms and cell treatments in stem cell models. This review will broadly benefit scientists, engineers and medical practitioners in the areas of stem cell research.
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Affiliation(s)
- Heng Zhou
- Center of Regenerative Medicine and Department of Stomatology, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Peng Ye
- Department of Pharmacy, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Wei Xiong
- Center of Regenerative Medicine and Department of Stomatology, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Xingxiang Duan
- Center of Regenerative Medicine and Department of Stomatology, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Shuili Jing
- Center of Regenerative Medicine and Department of Stomatology, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Yan He
- Institute of Regenerative and Translational Medicine, Tianyou Hospital of Wuhan University of Science and Technology, Wuhan, 430064, Hubei, People's Republic of China
- Department of Oral and Maxillofacial Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Zhi Zeng
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China.
| | - Yen Wei
- The Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, 100084, People's Republic of China.
| | - Qingsong Ye
- Center of Regenerative Medicine and Department of Stomatology, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China.
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3
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Nadig A, Replogle JM, Pogson AN, McCarroll SA, Weissman JS, Robinson EB, O’Connor LJ. Transcriptome-wide characterization of genetic perturbations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.03.601903. [PMID: 39005298 PMCID: PMC11244993 DOI: 10.1101/2024.07.03.601903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are often noisy due to cost and technical constraints, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. Within TRADE, we derive multiple novel, interpretable statistical metrics, including the "transcriptome-wide impact", an estimator of the overall transcriptional effect of a perturbation which is stable across sampling depths. We analyze new and published large-scale Perturb-seq datasets to show that many true transcriptional effects are not statistically significant, but detectable in aggregate with TRADE. In a genome-scale Perturb-seq screen, we find that a typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene perturbation affects over 500 genes. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We use this ability to identify perturbations with cell-type dependent effects and to find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, finding that transcriptomic effect correlations are greater than genetic correlations for these diagnoses. TRADE lays an analytic foundation for the systematic comparison of genetic perturbation atlases, as well as differential expression experiments more broadly.
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Affiliation(s)
- Ajay Nadig
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Joseph M. Replogle
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Angela N. Pogson
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jonathan S. Weissman
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elise B. Robinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Luke J. O’Connor
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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4
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Ivanov IE, Hirata-Miyasaki E, Chandler T, Cheloor-Kovilakam R, Liu Z, Pradeep S, Liu C, Bhave M, Khadka S, Arias C, Leonetti MD, Huang B, Mehta SB. Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.19.572435. [PMID: 38187521 PMCID: PMC10769231 DOI: 10.1101/2023.12.19.572435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy, an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 minutes over 7.5 hours. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.
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Affiliation(s)
- Ivan E. Ivanov
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | | | - Talon Chandler
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Rasmi Cheloor-Kovilakam
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, United States
| | - Ziwen Liu
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Soorya Pradeep
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Chad Liu
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Madhura Bhave
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Sudip Khadka
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Carolina Arias
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | | | - Bo Huang
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, United States
| | - Shalin B. Mehta
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
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5
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Kwon JJ, Pan J, Gonzalez G, Hahn WC, Zitnik M. On knowing a gene: A distributional hypothesis of gene function. Cell Syst 2024; 15:488-496. [PMID: 38810640 PMCID: PMC11189734 DOI: 10.1016/j.cels.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 02/25/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as "is-a" relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.
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Affiliation(s)
- Jason J Kwon
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Joshua Pan
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guadalupe Gonzalez
- Department of Computing, Faculty of Engineering, Imperial College, London SW7 2AZ, UK
| | - William C Hahn
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Marinka Zitnik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Department of Biomedical Informatics, Boston, MA 02115, USA; Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138, USA; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA 02134, USA.
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6
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Tang Q, Ratnayake R, Seabra G, Jiang Z, Fang R, Cui L, Ding Y, Kahveci T, Bian J, Li C, Luesch H, Li Y. Morphological profiling for drug discovery in the era of deep learning. Brief Bioinform 2024; 25:bbae284. [PMID: 38886164 PMCID: PMC11182685 DOI: 10.1093/bib/bbae284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/13/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
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Affiliation(s)
- Qiaosi Tang
- Calico Life Sciences, South San Francisco, CA 94080, United States
| | - Ranjala Ratnayake
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Gustavo Seabra
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Zhe Jiang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Ruogu Fang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Lina Cui
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yousong Ding
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Tamer Kahveci
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32611, United States
| | - Chenglong Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Hendrik Luesch
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yanjun Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
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7
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Cooper S, Obolenski S, Waters AJ, Bassett AR, Coelho MA. Analyzing the functional effects of DNA variants with gene editing. CELL REPORTS METHODS 2024; 4:100776. [PMID: 38744287 PMCID: PMC11133854 DOI: 10.1016/j.crmeth.2024.100776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/01/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.
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Affiliation(s)
- Sarah Cooper
- Cellular and Gene Editing Research, Wellcome Sanger Institute, Hinxton, UK
| | - Sofia Obolenski
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK; Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Andrew J Waters
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Andrew R Bassett
- Cellular and Gene Editing Research, Wellcome Sanger Institute, Hinxton, UK.
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8
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Ciucci G, Braga L, Zacchigna S. Discovery platforms for RNA therapeutics. Br J Pharmacol 2024. [PMID: 38760893 DOI: 10.1111/bph.16424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 04/14/2024] [Accepted: 04/19/2024] [Indexed: 05/20/2024] Open
Abstract
RNA therapeutics are emerging as a unique opportunity to drug currently "undruggable" molecules and diseases. While their advantages over conventional, small molecule drugs, their therapeutic implications and the tools for their effective in vivo delivery have been extensively reviewed, little attention has been so far paid to the technological platforms exploited for the discovery of RNA therapeutics. Here, we provide an overview of the existing platforms and ex vivo assays for RNA discovery, their advantages and disadvantages, as well as their main fields of application, with specific focus on RNA therapies that have reached either phase 3 or market approval.
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Affiliation(s)
- Giulio Ciucci
- Cardiovascular Biology Laboratory, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
| | - Luca Braga
- Functional Cell Biology Laboratory, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
| | - Serena Zacchigna
- Cardiovascular Biology Laboratory, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
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9
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Hsiung CCS, Wilson CM, Sambold NA, Dai R, Chen Q, Teyssier N, Misiukiewicz S, Arab A, O'Loughlin T, Cofsky JC, Shi J, Gilbert LA. Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations. Nat Biotechnol 2024:10.1038/s41587-024-02224-0. [PMID: 38760567 DOI: 10.1038/s41587-024-02224-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/28/2024] [Indexed: 05/19/2024]
Abstract
Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.
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Affiliation(s)
- C C-S Hsiung
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
| | - C M Wilson
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
- Tetrad Graduate Program, University of California, San Francisco, CA, USA
| | | | - R Dai
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Q Chen
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - N Teyssier
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - S Misiukiewicz
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - A Arab
- Arc Institute, Palo Alto, CA, USA
| | - T O'Loughlin
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - J C Cofsky
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - J Shi
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - L A Gilbert
- Department of Urology, University of California, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Arc Institute, Palo Alto, CA, USA.
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10
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Laubscher E, Wang X, Razin N, Dougherty T, Xu RJ, Ombelets L, Pao E, Graf W, Moffitt JR, Yue Y, Van Valen D. Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning. Cell Syst 2024; 15:475-482.e6. [PMID: 38754367 DOI: 10.1016/j.cels.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/05/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.
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Affiliation(s)
- Emily Laubscher
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA 91125, USA
| | - Xuefei Wang
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Nitzan Razin
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Tom Dougherty
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Rosalind J Xu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02115, USA
| | - Lincoln Ombelets
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA 91125, USA
| | - Edward Pao
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - William Graf
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yisong Yue
- Division of Computational and Mathematical Sciences, Caltech, Pasadena, CA 91125, USA
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
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11
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Wong CH, Wingett SW, Qian C, Hunter MR, Taliaferro JM, Ross-Thriepland D, Bullock SL. Genome-scale requirements for dynein-based transport revealed by a high-content arrayed CRISPR screen. J Cell Biol 2024; 223:e202306048. [PMID: 38448164 PMCID: PMC10916854 DOI: 10.1083/jcb.202306048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 01/10/2024] [Accepted: 02/19/2024] [Indexed: 03/08/2024] Open
Abstract
The microtubule motor dynein plays a key role in cellular organization. However, little is known about how dynein's biosynthesis, assembly, and functional diversity are orchestrated. To address this issue, we have conducted an arrayed CRISPR loss-of-function screen in human cells using the distribution of dynein-tethered peroxisomes and early endosomes as readouts. From a genome-wide gRNA library, 195 validated hits were recovered and parsed into those impacting multiple dynein cargoes and those whose effects are restricted to a subset of cargoes. Clustering of high-dimensional phenotypic fingerprints revealed co-functional proteins involved in many cellular processes, including several candidate novel regulators of core dynein functions. Further analysis of one of these factors, the RNA-binding protein SUGP1, indicates that it promotes cargo trafficking by sustaining functional expression of the dynein activator LIS1. Our data represent a rich source of new hypotheses for investigating microtubule-based transport, as well as several other aspects of cellular organization captured by our high-content imaging.
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Affiliation(s)
- Chun Hao Wong
- Cell Biology Division, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
- Centre for Genomic Research, Discovery Sciences, AstraZeneca, Cambridge, UK
| | - Steven W. Wingett
- Cell Biology Division, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
| | - Chen Qian
- Quantitative Biology, Discovery Sciences, AstraZeneca, Cambridge, UK
| | - Morag Rose Hunter
- Centre for Genomic Research, Discovery Sciences, AstraZeneca, Cambridge, UK
| | - J. Matthew Taliaferro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Simon L. Bullock
- Cell Biology Division, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
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12
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Sánchez Rivera FJ, Dow LE. How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research. Cold Spring Harb Perspect Med 2024; 14:a041384. [PMID: 37487630 PMCID: PMC11065179 DOI: 10.1101/cshperspect.a041384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.
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Affiliation(s)
- Francisco J Sánchez Rivera
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Lukas E Dow
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10065, USA
- Department of Biochemistry, Weill Cornell Medicine, New York, New York 10065, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York 10065, USA
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13
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Johnson GA, Gould SI, Sánchez-Rivera FJ. Deconstructing cancer with precision genome editing. Biochem Soc Trans 2024; 52:803-819. [PMID: 38629716 PMCID: PMC11088927 DOI: 10.1042/bst20230984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
Recent advances in genome editing technologies are allowing investigators to engineer and study cancer-associated mutations in their endogenous genetic contexts with high precision and efficiency. Of these, base editing and prime editing are quickly becoming gold-standards in the field due to their versatility and scalability. Here, we review the merits and limitations of these precision genome editing technologies, their application to modern cancer research, and speculate how these could be integrated to address future directions in the field.
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Affiliation(s)
- Grace A. Johnson
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
| | - Samuel I. Gould
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
| | - Francisco J. Sánchez-Rivera
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
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14
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McCauley KB, Kukreja K, Tovar Walker AE, Jaffe AB, Klein AM. A map of signaling responses in the human airway epithelium. Cell Syst 2024; 15:307-321.e10. [PMID: 38508187 PMCID: PMC11031335 DOI: 10.1016/j.cels.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 11/14/2023] [Accepted: 02/28/2024] [Indexed: 03/22/2024]
Abstract
Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal cell and secretory cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell-cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Katherine B McCauley
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Respiratory Diseases, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA; Disease Area X, Biomedical Research, Novartis, Cambridge, MA 02139, USA
| | - Kalki Kukreja
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Aron B Jaffe
- Respiratory Diseases, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
| | - Allon M Klein
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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15
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Gentili M, Carlson RJ, Liu B, Hellier Q, Andrews J, Qin Y, Blainey PC, Hacohen N. Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588166. [PMID: 38645119 PMCID: PMC11030420 DOI: 10.1101/2024.04.07.588166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
STING is an innate immune sensor that traffics across many cellular compartments to carry out its function of detecting cyclic di-nucleotides and triggering defense processes. Mutations in factors that regulate this process are often linked to STING-dependent human inflammatory disorders. To systematically identify factors involved in STING trafficking, we performed a genome-wide optical pooled screen and examined the impact of genetic perturbations on intracellular STING localization. Based on subcellular imaging of STING protein and trafficking markers in 45 million cells perturbed with sgRNAs, we defined 464 clusters of gene perturbations with similar cellular phenotypes. A higher-dimensional focused optical pooled screen on 262 perturbed genes which assayed 11 imaging channels identified 73 finer phenotypic clusters. In a cluster containing USE1, a protein that mediates Golgi to ER transport, we found a gene of unknown function, C19orf25. Consistent with the known role of USE1, loss of C19orf25 enhanced STING signaling. Other clusters contained subunits of the HOPS, GARP and RIC1-RGP1 complexes. We show that HOPS deficiency delayed STING degradation and consequently increased signaling. Similarly, GARP/RIC1-RGP1 loss increased STING signaling by delaying STING exit from the Golgi. Our findings demonstrate that genome-wide genotype-phenotype maps based on high-content cell imaging outperform other screening approaches, and provide a community resource for mining for factors that impact STING trafficking as well as other cellular processes observable in our dataset.
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16
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Carlson RJ, Patten JJ, Stefanakis G, Soong BY, Radhakrishnan A, Singh A, Thakur N, Amarasinghe GK, Hacohen N, Basler CF, Leung D, Uhler C, Davey RA, Blainey PC. Single-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.06.588168. [PMID: 38617272 PMCID: PMC11014611 DOI: 10.1101/2024.04.06.588168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Ebola virus (EBOV) is a high-consequence filovirus that gives rise to frequent epidemics with high case fatality rates and few therapeutic options. Here, we applied image-based screening of a genome-wide CRISPR library to systematically identify host cell regulators of Ebola virus infection in 39,085,093 million single cells. Measuring viral RNA and protein levels together with their localization in cells identified over 998 related host factors and provided detailed information about the role of each gene across the virus replication cycle. We trained a deep learning model on single-cell images to associate each host factor with predicted replication steps, and confirmed the predicted relationship for select host factors. Among the findings, we showed that the mitochondrial complex III subunit UQCRB is a post-entry regulator of Ebola virus RNA replication, and demonstrated that UQCRB inhibition with a small molecule reduced overall Ebola virus infection with an IC50 of 5 μM. Using a random forest model, we also identified perturbations that reduced infection by disrupting the equilibrium between viral RNA and protein. One such protein, STRAP, is a spliceosome-associated factor that was found to be closely associated with VP35, a viral protein required for RNA processing. Loss of STRAP expression resulted in a reduction in full-length viral genome production and subsequent production of non-infectious virus particles. Overall, the data produced in this genome-wide high-content single-cell screen and secondary screens in additional cell lines and related filoviruses (MARV and SUDV) revealed new insights about the role of host factors in virus replication and potential new targets for therapeutic intervention.
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Affiliation(s)
- Rebecca J Carlson
- Massachusetts Institute of Technology, Department of Health Sciences and Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J J Patten
- Department of Virology, Immunology, and Microbiology, Boston University School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - George Stefanakis
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian Y Soong
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adityanarayanan Radhakrishnan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Avtar Singh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Naveen Thakur
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gaya K Amarasinghe
- Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital, Cancer Center, Boston, MA, USA
| | - Christopher F Basler
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daisy Leung
- Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
| | - Caroline Uhler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert A Davey
- Department of Virology, Immunology, and Microbiology, Boston University School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
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17
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Tsubouchi A, An Y, Kawamura Y, Yanagihashi Y, Nakayama H, Murata Y, Teranishi K, Ishiguro S, Aburatani H, Yachie N, Ota S. Pooled CRISPR screening of high-content cellular phenotypes using ghost cytometry. CELL REPORTS METHODS 2024; 4:100737. [PMID: 38531306 PMCID: PMC10985231 DOI: 10.1016/j.crmeth.2024.100737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/30/2023] [Accepted: 02/27/2024] [Indexed: 03/28/2024]
Abstract
Recent advancements in image-based pooled CRISPR screening have facilitated the mapping of diverse genotype-phenotype associations within mammalian cells. However, the rapid enrichment of cells based on morphological information continues to pose a challenge, constraining the capacity for large-scale gene perturbation screening across diverse high-content cellular phenotypes. In this study, we demonstrate the applicability of multimodal ghost cytometry-based cell sorting, including both fluorescent and label-free high-content phenotypes, for rapid pooled CRISPR screening within vast cell populations. Using the high-content cell sorter operating in fluorescence mode, we successfully executed kinase-specific CRISPR screening targeting genes influencing the nuclear translocation of RelA. Furthermore, using the multiparametric, label-free mode, we performed large-scale screening to identify genes involved in macrophage polarization. Notably, the label-free platform can enrich target phenotypes without requiring invasive staining, preserving untouched cells for downstream assays and expanding the potential for screening cellular phenotypes even when suitable markers are absent.
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Affiliation(s)
| | - Yuri An
- ThinkCyte Inc., Tokyo 113-8654, Japan
| | | | | | | | | | | | - Soh Ishiguro
- School of Biomedical Engineering, Faculty of Medicine and Faculty of Applied Science, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Hiroyuki Aburatani
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
| | - Nozomu Yachie
- School of Biomedical Engineering, Faculty of Medicine and Faculty of Applied Science, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
| | - Sadao Ota
- ThinkCyte Inc., Tokyo 113-8654, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan.
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18
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Walton RT, Qin Y, Blainey PC. CROPseq-multi: a versatile solution for multiplexed perturbation and decoding in pooled CRISPR screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585235. [PMID: 38558968 PMCID: PMC10979941 DOI: 10.1101/2024.03.17.585235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Forward genetic screens seek to dissect complex biological systems by systematically perturbing genetic elements and observing the resulting phenotypes. While standard screening methodologies introduce individual perturbations, multiplexing perturbations improves the performance of single-target screens and enables combinatorial screens for the study of genetic interactions. Current tools for multiplexing perturbations are incompatible with pooled screening methodologies that require mRNA-embedded barcodes, including some microscopy and single cell sequencing approaches. Here, we report the development of CROPseq-multi, a CROPseq1-inspired lentiviral system to multiplex Streptococcus pyogenes (Sp) Cas9-based perturbations with mRNA-embedded barcodes. CROPseq-multi has equivalent per-guide activity to CROPseq and low lentiviral recombination frequencies. CROPseq-multi is compatible with enrichment screening methodologies and optical pooled screens, and is extensible to screens with single-cell sequencing readouts. For optical pooled screens, an optimized and multiplexed in situ detection protocol improves barcode detection efficiency 10-fold, enables detection of recombination events, and increases decoding efficiency 3-fold relative to CROPseq. CROPseq-multi is a widely applicable multiplexing solution for diverse SpCas9-based genetic screening approaches.
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Affiliation(s)
- Russell T. Walton
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Yue Qin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paul C. Blainey
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
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19
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Shkarina K, Broz P. Selective induction of programmed cell death using synthetic biology tools. Semin Cell Dev Biol 2024; 156:74-92. [PMID: 37598045 DOI: 10.1016/j.semcdb.2023.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/21/2023] [Accepted: 07/21/2023] [Indexed: 08/21/2023]
Abstract
Regulated cell death (RCD) controls the removal of dispensable, infected or malignant cells, and is thus essential for development, homeostasis and immunity of multicellular organisms. Over the last years different forms of RCD have been described (among them apoptosis, necroptosis, pyroptosis and ferroptosis), and the cellular signaling pathways that control their induction and execution have been characterized at the molecular level. It has also become apparent that different forms of RCD differ in their capacity to elicit inflammation or an immune response, and that RCD pathways show a remarkable plasticity. Biochemical and genetic studies revealed that inhibition of a given pathway often results in the activation of back-up cell death mechanisms, highlighting close interconnectivity based on shared signaling components and the assembly of multivalent signaling platforms that can initiate different forms of RCD. Due to this interconnectivity and the pleiotropic effects of 'classical' cell death inducers, it is challenging to study RCD pathways in isolation. This has led to the development of tools based on synthetic biology that allow the targeted induction of RCD using chemogenetic or optogenetic methods. Here we discuss recent advances in the development of such toolset, highlighting their advantages and limitations, and their application for the study of RCD in cells and animals.
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Affiliation(s)
- Kateryna Shkarina
- Institute of Innate Immunity, University Hospital Bonn, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
| | - Petr Broz
- Department of Immunobiology, University of Lausanne, Switzerland.
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20
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Israel U, Marks M, Dilip R, Li Q, Yu C, Laubscher E, Li S, Schwartz M, Pradhan E, Ates A, Abt M, Brown C, Pao E, Pearson-Goulart A, Perona P, Gkioxari G, Barnowski R, Yue Y, Valen DV. A Foundation Model for Cell Segmentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.17.567630. [PMID: 38045277 PMCID: PMC10690226 DOI: 10.1101/2023.11.17.567630] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models in use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive. In this work, we present CellSAM, a foundation model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells (in tissues and cell culture), yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. We also show that CellSAM can unify bioimaging analysis workflows such as spatial transcriptomics and cell tracking. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.
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Affiliation(s)
- Uriah Israel
- Division of Biology and Biological Engineering, Caltech
- Division of Computing and Mathematical Science, Caltech
| | - Markus Marks
- Division of Engineering and Applied Science, Caltech
- Division of Computing and Mathematical Science, Caltech
| | - Rohit Dilip
- Division of Computing and Mathematical Science, Caltech
| | - Qilin Li
- Division of Engineering and Applied Science, Caltech
| | - Changhua Yu
- Division of Biology and Biological Engineering, Caltech
| | | | - Shenyi Li
- Division of Biology and Biological Engineering, Caltech
| | | | - Elora Pradhan
- Division of Biology and Biological Engineering, Caltech
| | - Ada Ates
- Division of Biology and Biological Engineering, Caltech
| | - Martin Abt
- Division of Biology and Biological Engineering, Caltech
| | - Caitlin Brown
- Division of Biology and Biological Engineering, Caltech
| | - Edward Pao
- Division of Biology and Biological Engineering, Caltech
| | | | - Pietro Perona
- Division of Engineering and Applied Science, Caltech
- Division of Computing and Mathematical Science, Caltech
| | | | | | - Yisong Yue
- Division of Computing and Mathematical Science, Caltech
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech
- Howard Hughes Medical Institute
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21
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Hsiung CC, Wilson CM, Sambold NA, Dai R, Chen Q, Misiukiewicz S, Arab A, Teyssier N, O'Loughlin T, Cofsky JC, Shi J, Gilbert LA. Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.558350. [PMID: 37781594 PMCID: PMC10541102 DOI: 10.1101/2023.09.18.558350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order ( > 3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as mul tiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.
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22
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Laubscher E, Wang X(J, Razin N, Dougherty T, Xu RJ, Ombelets L, Pao E, Graf W, Moffitt JR, Yue Y, Van Valen D. Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.03.556122. [PMID: 37732188 PMCID: PMC10508757 DOI: 10.1101/2023.09.03.556122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually-tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from MERFSIH, seqFISH, or ISS experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.
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Affiliation(s)
- Emily Laubscher
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA
| | | | - Nitzan Razin
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Tom Dougherty
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Rosalind J. Xu
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston MA
- Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
| | - Lincoln Ombelets
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA
| | - Edward Pao
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - William Graf
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Jeffrey R. Moffitt
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston MA
- Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA
- Broad Institute of Harvard and MIT, Cambridge, MA
| | - Yisong Yue
- Division of Computational and Mathematical Sciences, Caltech, Pasadena, CA
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
- Howard Hughes Medical Institute, Chevy Chase, MD
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23
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Morris JA, Sun JS, Sanjana NE. Next-generation forward genetic screens: uniting high-throughput perturbations with single-cell analysis. Trends Genet 2024; 40:118-133. [PMID: 37989654 PMCID: PMC10872607 DOI: 10.1016/j.tig.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023]
Abstract
Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping.
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Affiliation(s)
- John A Morris
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA
| | - Jennifer S Sun
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA
| | - Neville E Sanjana
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
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24
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Raghavan A, Pirruccello JP, Ellinor PT, Lindsay ME. Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease. Arterioscler Thromb Vasc Biol 2024; 44:334-351. [PMID: 38095107 PMCID: PMC10843699 DOI: 10.1161/atvbaha.123.318771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/21/2023] [Indexed: 01/04/2024]
Abstract
Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.
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Affiliation(s)
- Avanthi Raghavan
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - James P. Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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25
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Yang C, Lei Y, Ren T, Yao M. The Current Situation and Development Prospect of Whole-Genome Screening. Int J Mol Sci 2024; 25:658. [PMID: 38203828 PMCID: PMC10779205 DOI: 10.3390/ijms25010658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/22/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
High-throughput genetic screening is useful for discovering critical genes or gene sequences that trigger specific cell functions and/or phenotypes. Loss-of-function genetic screening is mainly achieved through RNA interference (RNAi), CRISPR knock-out (CRISPRko), and CRISPR interference (CRISPRi) technologies. Gain-of-function genetic screening mainly depends on the overexpression of a cDNA library and CRISPR activation (CRISPRa). Base editing can perform both gain- and loss-of-function genetic screening. This review discusses genetic screening techniques based on Cas9 nuclease, including Cas9-mediated genome knock-out and dCas9-based gene activation and interference. We compare these methods with previous genetic screening techniques based on RNAi and cDNA library overexpression and propose future prospects and applications for CRISPR screening.
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Affiliation(s)
| | | | | | - Mingze Yao
- Shanxi Provincial Key Laboratory for Medical Molecular Cell Biology, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education and Institute of Biomedical Sciences, Shanxi University, Taiyuan 030006, China; (C.Y.); (Y.L.); (T.R.)
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26
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Gu J, Iyer A, Wesley B, Taglialatela A, Leuzzi G, Hangai S, Decker A, Gu R, Klickstein N, Shuai Y, Jankovic K, Parker-Burns L, Jin Y, Zhang JY, Hong J, Niu S, Chou J, Landau DA, Azizi E, Chan EM, Ciccia A, Gaublomme JT. CRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.26.572587. [PMID: 38234835 PMCID: PMC10793456 DOI: 10.1101/2023.12.26.572587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Pooled genetic screens are powerful tools to study gene function in a high-throughput manner. Typically, sequencing-based screens require cell lysis, which limits the examination of critical phenotypes such as cell morphology, protein subcellular localization, and cell-cell/tissue interactions. In contrast, emerging optical pooled screening methods enable the investigation of these spatial phenotypes in response to targeted CRISPR perturbations. In this study, we report a multi-omic optical pooled CRISPR screening method, which we have named CRISPRmap. Our method combines a novel in situ CRISPR guide identifying barcode readout approach with concurrent multiplexed immunofluorescence and in situ RNA detection. CRISPRmap barcodes are detected and read out through combinatorial hybridization of DNA oligos, enhancing barcode detection efficiency, while reducing both dependency on third party proprietary sequencing reagents and assay cost. Notably, we conducted a multi-omic base-editing screen in a breast cancer cell line on core DNA damage repair genes involved in the homologous recombination and Fanconi anemia pathways investigating how nucleotide variants in those genes influence DNA damage signaling and cell cycle regulation following treatment with ionizing radiation or DNA damaging agents commonly used for cancer therapy. Approximately a million cells were profiled with our multi-omic approach, providing a comprehensive phenotypic assessment of the functional consequences of the studied variants. CRISPRmap enabled us to pinpoint likely-pathogenic patient-derived mutations that were previously classified as variants of unknown clinical significance. Furthermore, our approach effectively distinguished barcodes of a pooled library in tumor tissue, and we coupled it with cell-type and molecular phenotyping by cyclic immunofluorescence. Multi-omic spatial analysis of how CRISPR-perturbed cells respond to various environmental cues in the tissue context offers the potential to significantly expand our understanding of tissue biology in both health and disease.
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Affiliation(s)
- Jiacheng Gu
- Department of Biological Sciences, Columbia University, NY, USA
| | - Abhishek Iyer
- Department of Biological Sciences, Columbia University, NY, USA
| | - Ben Wesley
- Department of Biological Sciences, Columbia University, NY, USA
| | - Angelo Taglialatela
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
| | - Giuseppe Leuzzi
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- Institute for Cancer Genetics, Columbia University Irving Medical Center, NY, USA
| | - Sho Hangai
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | | | - Ruoyu Gu
- Department of Biological Sciences, Columbia University, NY, USA
| | | | - Yuanlong Shuai
- Department of Biological Sciences, Columbia University, NY, USA
| | - Kristina Jankovic
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | - Lucy Parker-Burns
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | - Yinuo Jin
- Department of Biomedical Engineering, Columbia University, NY, USA
| | - Jia Yi Zhang
- Department of Biomedical Engineering, Columbia University, NY, USA
| | - Justin Hong
- Department of Computer Science, Columbia University, NY, USA
| | - Steve Niu
- Weill Cornell Medicine, NY, USA
- Genentech Research and Early Development, CA, USA
| | - Jacqueline Chou
- Department of Biological Sciences, Columbia University, NY, USA
- Weill Cornell Medicine, NY, USA
| | - Dan A. Landau
- Weill Cornell Medicine, NY, USA
- New York Genome Center, NY, USA
| | - Elham Azizi
- Department of Biomedical Engineering, Columbia University, NY, USA
- Department of Computer Science, Columbia University, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, NY, USA
| | - Edmond M. Chan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- New York Genome Center, NY, USA
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, NY, USA
| | - Alberto Ciccia
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- Institute for Cancer Genetics, Columbia University Irving Medical Center, NY, USA
| | - Jellert T. Gaublomme
- Department of Biological Sciences, Columbia University, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- New York Genome Center, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, NY, USA
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27
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Binan L, Danquah S, Valakh V, Simonton B, Bezney J, Nehme R, Cleary B, Farhi SL. Simultaneous CRISPR screening and spatial transcriptomics reveals intracellular, intercellular, and functional transcriptional circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.30.569494. [PMID: 38076932 PMCID: PMC10705493 DOI: 10.1101/2023.11.30.569494] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Pooled optical screens have enabled the study of cellular interactions, morphology, or dynamics at massive scale, but have not yet leveraged the power of highly-plexed single-cell resolved transcriptomic readouts to inform molecular pathways. Here, we present Perturb-FISH, which bridges these approaches by combining imaging spatial transcriptomics with parallel optical detection of in situ amplified guide RNAs. We show that Perturb-FISH recovers intracellular effects that are consistent with Perturb-seq results in a screen of lipopolysaccharide response in cultured monocytes, and uncover new intercellular and density-dependent regulation of the innate immune response. We further pair Perturb-FISH with a functional readout in a screen of autism spectrum disorder risk genes, showing common calcium activity phenotypes in induced pluripotent stem cell derived astrocytes and their associated genetic interactions and dysregulated molecular pathways. Perturb-FISH is thus a generally applicable method for studying the genetic and molecular associations of spatial and functional biology at single-cell resolution.
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Affiliation(s)
- Loϊc Binan
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Serwah Danquah
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Vera Valakh
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Brooke Simonton
- Present address: The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. (Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA)
| | - Jon Bezney
- Present address: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. (Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA)
| | - Ralda Nehme
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Brian Cleary
- Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA; Program in Bioinformatics, Boston University, Boston, MA, USA; Biological Design Center, Boston University, Boston, MA, USA
| | - Samouil L Farhi
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
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28
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Zhang R, Huang Y, Li M, Wang L, Li B, Xia A, Li Y, Yang S, Jin F. High-throughput, microscopy-based screening and quantification of genetic elements. MLIFE 2023; 2:450-461. [PMID: 38818273 PMCID: PMC10989126 DOI: 10.1002/mlf2.12096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/08/2023] [Accepted: 10/10/2023] [Indexed: 06/01/2024]
Abstract
Synthetic biology relies on the screening and quantification of genetic components to assemble sophisticated gene circuits with specific functions. Microscopy is a powerful tool for characterizing complex cellular phenotypes with increasing spatial and temporal resolution to library screening of genetic elements. Microscopy-based assays are powerful tools for characterizing cellular phenotypes with spatial and temporal resolution and can be applied to large-scale samples for library screening of genetic elements. However, strategies for high-throughput microscopy experiments remain limited. Here, we present a high-throughput, microscopy-based platform that can simultaneously complete the preparation of an 8 × 12-well agarose pad plate, allowing for the screening of 96 independent strains or experimental conditions in a single experiment. Using this platform, we screened a library of natural intrinsic promoters from Pseudomonas aeruginosa and identified a small subset of robust promoters that drives stable levels of gene expression under varying growth conditions. Additionally, the platform allowed for single-cell measurement of genetic elements over time, enabling the identification of complex and dynamic phenotypes to map genotype in high throughput. We expected that the platform could be employed to accelerate the identification and characterization of genetic elements in various biological systems, as well as to understand the relationship between cellular phenotypes and internal states, including genotypes and gene expression programs.
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Affiliation(s)
- Rongrong Zhang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Yajia Huang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Mei Li
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Lei Wang
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Bing Li
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Aiguo Xia
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Ye Li
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Shuai Yang
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- Chengdu Documentation and Information CenterChinese Academy of SciencesChengduChina
| | - Fan Jin
- CAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- Shenzhen Synthetic Biology InfrastructureShenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
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29
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Mellis IA, Bodkin N, Melzer ME, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.14.553318. [PMID: 37645989 PMCID: PMC10462021 DOI: 10.1101/2023.08.14.553318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A. Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Madeline E. Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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30
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Schraivogel D, Steinmetz LM, Parts L. Pooled Genome-Scale CRISPR Screens in Single Cells. Annu Rev Genet 2023; 57:223-244. [PMID: 37562410 DOI: 10.1146/annurev-genet-072920-013842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting-based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.
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Affiliation(s)
- Daniel Schraivogel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany;
| | - Lars M Steinmetz
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany;
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
- Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California, USA
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31
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Meyers S, Demeyer S, Cools J. CRISPR screening in hematology research: from bulk to single-cell level. J Hematol Oncol 2023; 16:107. [PMID: 37875911 PMCID: PMC10594891 DOI: 10.1186/s13045-023-01495-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/21/2023] [Indexed: 10/26/2023] Open
Abstract
The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.
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Affiliation(s)
- Sarah Meyers
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Sofie Demeyer
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Jan Cools
- Center for Human Genetics, KU Leuven, Leuven, Belgium.
- Center for Cancer Biology, VIB, Leuven, Belgium.
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium.
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32
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Sunshine S, Puschnik AS, Replogle JM, Laurie MT, Liu J, Zha BS, Nuñez JK, Byrum JR, McMorrow AH, Frieman MB, Winkler J, Qiu X, Rosenberg OS, Leonetti MD, Ye CJ, Weissman JS, DeRisi JL, Hein MY. Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq. Nat Commun 2023; 14:6245. [PMID: 37803001 PMCID: PMC10558542 DOI: 10.1038/s41467-023-41788-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/15/2023] [Indexed: 10/08/2023] Open
Abstract
Genomic and proteomic screens have identified numerous host factors of SARS-CoV-2, but efficient delineation of their molecular roles during infection remains a challenge. Here we use Perturb-seq, combining genetic perturbations with a single-cell readout, to investigate how inactivation of host factors changes the course of SARS-CoV-2 infection and the host response in human lung epithelial cells. Our high-dimensional data resolve complex phenotypes such as shifts in the stages of infection and modulations of the interferon response. However, only a small percentage of host factors showed such phenotypes upon perturbation. We further identified the NF-κB inhibitor IκBα (NFKBIA), as well as the translation factors EIF4E2 and EIF4H as strong host dependency factors acting early in infection. Overall, our study provides massively parallel functional characterization of host factors of SARS-CoV-2 and quantitatively defines their roles both in virus-infected and bystander cells.
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Affiliation(s)
- Sara Sunshine
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | | | - Joseph M Replogle
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Matthew T Laurie
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Jamin Liu
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
- University of California, Berkeley-UCSF Joint Graduate Program in Bioengineering, San Francisco, CA, USA
| | - Beth Shoshana Zha
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - James K Nuñez
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Janie R Byrum
- Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
| | | | - Matthew B Frieman
- Department of Microbiology and Immunology, Center for Pathogen Research, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Juliane Winkler
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, USA
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Oren S Rosenberg
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Institute of Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Joseph L DeRisi
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA.
| | - Marco Y Hein
- Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA.
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna, Austria.
- Medical University of Vienna, Center for Medical Biochemistry, Vienna, Austria.
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33
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Stossi F, Singh PK, Safari K, Marini M, Labate D, Mancini MA. High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery. Biochem Pharmacol 2023; 216:115770. [PMID: 37660829 DOI: 10.1016/j.bcp.2023.115770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.
| | - Pankaj K Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Kazem Safari
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Michela Marini
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Demetrio Labate
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
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34
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Zheng X, Wu B, Liu Y, Simmons SK, Kim K, Clarke GS, Ashiq A, Park J, Wang Z, Tong L, Wang Q, Xu X, Levin JZ, Jin X. Massively parallel in vivo Perturb-seq reveals cell type-specific transcriptional networks in cortical development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558077. [PMID: 37790302 PMCID: PMC10542124 DOI: 10.1101/2023.09.18.558077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Systematic analysis of gene function across diverse cell types in vivo is hindered by two challenges: obtaining sufficient cells from live tissues and accurately identifying each cell's perturbation in high-throughput single-cell assays. Leveraging AAV's versatile cell type tropism and high labeling capacity, we expanded the resolution and scale of in vivo CRISPR screens: allowing phenotypic analysis at single-cell resolution across a multitude of cell types in the embryonic brain, adult brain, and peripheral nervous system. We undertook extensive tests of 86 AAV serotypes, combined with a transposon system, to substantially amplify labeling and accelerate in vivo gene delivery from weeks to days. Using this platform, we performed an in utero genetic screen as proof-of-principle and identified pleiotropic regulatory networks of Foxg1 in cortical development, including Layer 6 corticothalamic neurons where it tightly controls distinct networks essential for cell fate specification. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% (mediated by lentivirus), and achieve analysis of over 30,000 cells in one experiment, thus enabling massively parallel in vivo Perturb-seq. Compatible with various perturbation techniques (CRISPRa/i) and phenotypic measurements (single-cell or spatial multi-omics), our platform presents a flexible, modular approach to interrogate gene function across diverse cell types in vivo, connecting gene variants to their causal functions.
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Affiliation(s)
- Xinhe Zheng
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
| | - Boli Wu
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
| | - Yuejia Liu
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
| | - Sean K. Simmons
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kwanho Kim
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Grace S. Clarke
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
| | - Abdullah Ashiq
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
| | - Joshua Park
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
| | - Zhilin Wang
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
| | - Liqi Tong
- Center for Neural Circuit Mapping, Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA
| | - Qizhao Wang
- Center for Neural Circuit Mapping, Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA
| | - Xiangmin Xu
- Center for Neural Circuit Mapping, Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA
| | - Joshua Z. Levin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Jin
- Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA
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35
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Lacoste J, Haghighi M, Haider S, Lin ZY, Segal D, Reno C, Qian WW, Xiong X, Shafqat-Abbasi H, Ryder PV, Senft R, Cimini BA, Roth FP, Calderwood M, Hill D, Vidal M, Yi SS, Sahni N, Peng J, Gingras AC, Singh S, Carpenter AE, Taipale M. Pervasive mislocalization of pathogenic coding variants underlying human disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.05.556368. [PMID: 37732209 PMCID: PMC10508771 DOI: 10.1101/2023.09.05.556368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease-causing. This creates a new bottleneck: determining the functional impact of each variant - largely a painstaking, customized process undertaken one or a few genes or variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,547 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of unknown significance. Our publicly available resource will likely accelerate the understanding of coding variation in human diseases.
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Affiliation(s)
- Jessica Lacoste
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Canada
- These authors contributed equally
| | - Marzieh Haghighi
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- These authors contributed equally
| | - Shahan Haider
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Canada
| | - Zhen-Yuan Lin
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
| | - Dmitri Segal
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Canada
| | - Chloe Reno
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Canada
| | - Wesley Wei Qian
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Xueting Xiong
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Canada
| | | | | | - Rebecca Senft
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Frederick P. Roth
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Michael Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - David Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - S. Stephen Yi
- Livestrong Cancer Institutes, Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX, USA
- Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA
- Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, TX, USA
| | - Jian Peng
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
| | | | | | - Mikko Taipale
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Canada
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36
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Jang MJ, Coughlin GM, Jackson CR, Chen X, Chuapoco MR, Vendemiatti JL, Wang AZ, Gradinaru V. Spatial transcriptomics for profiling the tropism of viral vectors in tissues. Nat Biotechnol 2023; 41:1272-1286. [PMID: 36702899 PMCID: PMC10443732 DOI: 10.1038/s41587-022-01648-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 12/15/2022] [Indexed: 01/27/2023]
Abstract
A barrier to advancing engineered adeno-associated viral vectors (AAVs) for precision access to cell subtypes is a lack of high-throughput, high-resolution assays to characterize in vivo transduction profiles. In this study, we developed an ultrasensitive, sequential fluorescence in situ hybridization (USeqFISH) method for spatial transcriptomic profiling of endogenous and viral RNA with a short barcode in intact tissue volumes by integrating hydrogel-based tissue clearing, enhanced signal amplification and multiplexing using sequential labeling. Using USeqFISH, we investigated the transduction and cell subtype tropisms across mouse brain regions of six systemic AAVs, including AAV-PHP.AX, a new variant that transduces robustly and efficiently across neurons and astrocytes. Here we reveal distinct cell subtype biases of each AAV variant, including a bias of AAV-PHP.N toward excitatory neurons. USeqFISH also enables profiling of pooled regulatory cargos, as we show for a 13-variant pool of microRNA target sites in AAV genomes. Lastly, we demonstrate potential applications of USeqFISH for in situ AAV profiling and multimodal single-cell analysis in non-human primates.
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Affiliation(s)
- Min J Jang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Gerard M Coughlin
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Cameron R Jackson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Xinhong Chen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Miguel R Chuapoco
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Julia L Vendemiatti
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Alexander Z Wang
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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37
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Ramezani M, Bauman J, Singh A, Weisbart E, Yong J, Lozada M, Way GP, Kavari SL, Diaz C, Haghighi M, Batista TM, Pérez-Schindler J, Claussnitzer M, Singh S, Cimini BA, Blainey PC, Carpenter AE, Jan CH, Neal JT. A genome-wide atlas of human cell morphology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.06.552164. [PMID: 37609130 PMCID: PMC10441312 DOI: 10.1101/2023.08.06.552164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
A key challenge of the modern genomics era is developing data-driven representations of gene function. Here, we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-scale genotype-phenotype maps comprising >20,000 single-gene CRISPR-Cas9-based knockout experiments in >30 million cells. Our optical pooled cell profiling approach (PERISCOPE) combines a de-stainable high-dimensional phenotyping panel (based on Cell Painting1,2) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This approach provides high-dimensional phenotypic profiles of individual cells, while simultaneously enabling interrogation of subcellular processes. Our atlas reconstructs known pathways and protein-protein interaction networks, identifies culture media-specific responses to gene knockout, and clusters thousands of human genes by phenotypic similarity. Using this atlas, we identify the poorly-characterized disease-associated transmembrane protein TMEM251/LYSET as a Golgi-resident protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes, showing the power of these representations. In sum, our atlas and screening technology represent a rich and accessible resource for connecting genes to cellular functions at scale.
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Affiliation(s)
- Meraj Ramezani
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Type 2 Diabetes Systems Genomics Initiative of the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Bauman
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Current address: Stanford University, Stanford, CA, USA
| | - Avtar Singh
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Current address: Genentech Department of Cellular and Tissue Genomics, South San Francisco, CA, USA
| | - Erin Weisbart
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
| | - John Yong
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Maria Lozada
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Type 2 Diabetes Systems Genomics Initiative of the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gregory P Way
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Current address: Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Sanam L Kavari
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Current address: University of Pennsylvania, Philadelphia, PA, USA
| | - Celeste Diaz
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Current address: Stanford University, Stanford, CA, USA
| | | | - Thiago M Batista
- Type 2 Diabetes Systems Genomics Initiative of the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease at Broad Institute, Cambridge, MA, USA
| | - Joaquín Pérez-Schindler
- Type 2 Diabetes Systems Genomics Initiative of the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease at Broad Institute, Cambridge, MA, USA
| | - Melina Claussnitzer
- Type 2 Diabetes Systems Genomics Initiative of the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease at Broad Institute, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Beth A Cimini
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
| | - Paul C Blainey
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- MIT Department of Biological Engineering, Cambridge, MA, USA
- Koch Institute for Integrative Research at MIT, Cambridge, MA, USA
| | | | - Calvin H Jan
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - James T Neal
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Type 2 Diabetes Systems Genomics Initiative of the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease at Broad Institute, Cambridge, MA, USA
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38
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Armendariz DA, Sundarrajan A, Hon GC. Breaking enhancers to gain insights into developmental defects. eLife 2023; 12:e88187. [PMID: 37497775 PMCID: PMC10374278 DOI: 10.7554/elife.88187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/19/2023] [Indexed: 07/28/2023] Open
Abstract
Despite ground-breaking genetic studies that have identified thousands of risk variants for developmental diseases, how these variants lead to molecular and cellular phenotypes remains a gap in knowledge. Many of these variants are non-coding and occur at enhancers, which orchestrate key regulatory programs during development. The prevailing paradigm is that non-coding variants alter the activity of enhancers, impacting gene expression programs, and ultimately contributing to disease risk. A key obstacle to progress is the systematic functional characterization of non-coding variants at scale, especially since enhancer activity is highly specific to cell type and developmental stage. Here, we review the foundational studies of enhancers in developmental disease and current genomic approaches to functionally characterize developmental enhancers and their variants at scale. In the coming decade, we anticipate systematic enhancer perturbation studies to link non-coding variants to molecular mechanisms, changes in cell state, and disease phenotypes.
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Affiliation(s)
- Daniel A Armendariz
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, United States
| | - Anjana Sundarrajan
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, United States
| | - Gary C Hon
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, United States
- Hamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
- Lyda Hill Department of Bioinformatics, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, United States
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39
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Shevade K, Peddada S, Mader K, Przybyla L. Functional genomics in stem cell models: considerations and applications. Front Cell Dev Biol 2023; 11:1236553. [PMID: 37554308 PMCID: PMC10404852 DOI: 10.3389/fcell.2023.1236553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/13/2023] [Indexed: 08/10/2023] Open
Abstract
Protocols to differentiate human pluripotent stem cells have advanced in terms of cell type specificity and tissue-level complexity over the past 2 decades, which has facilitated human disease modeling in the most relevant cell types. The ability to generate induced PSCs (iPSCs) from patients further enables the study of disease mutations in an appropriate cellular context to reveal the mechanisms that underlie disease etiology and progression. As iPSC-derived disease models have improved in robustness and scale, they have also been adopted more widely for use in drug screens to discover new therapies and therapeutic targets. Advancement in genome editing technologies, in particular the discovery of CRISPR-Cas9, has further allowed for rapid development of iPSCs containing disease-causing mutations. CRISPR-Cas9 technologies have now evolved beyond creating single gene edits, aided by the fusion of inhibitory (CRISPRi) or activation (CRISPRa) domains to a catalytically dead Cas9 protein, enabling inhibition or activation of endogenous gene loci. These tools have been used in CRISPR knockout, CRISPRi, or CRISPRa screens to identify genetic modifiers that synergize or antagonize with disease mutations in a systematic and unbiased manner, resulting in identification of disease mechanisms and discovery of new therapeutic targets to accelerate drug discovery research. However, many technical challenges remain when applying large-scale functional genomics approaches to differentiated PSC populations. Here we review current technologies in the field of iPSC disease modeling and CRISPR-based functional genomics screens and practical considerations for implementation across a range of modalities, applications, and disease areas, as well as explore CRISPR screens that have been performed in iPSC models to-date and the insights and therapies these screens have produced.
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Affiliation(s)
- Kaivalya Shevade
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
| | - Sailaja Peddada
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
| | - Karl Mader
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
| | - Laralynne Przybyla
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
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40
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Kornienko J, Rodríguez-Martínez M, Fenzl K, Hinze F, Schraivogel D, Grosch M, Tunaj B, Lindenhofer D, Schraft L, Kueblbeck M, Smith E, Mao C, Brown E, Owens A, Saguner AM, Meder B, Parikh V, Gotthardt M, Steinmetz LM. Mislocalization of pathogenic RBM20 variants in dilated cardiomyopathy is caused by loss-of-interaction with Transportin-3. Nat Commun 2023; 14:4312. [PMID: 37463913 DOI: 10.1038/s41467-023-39965-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
Severe forms of dilated cardiomyopathy (DCM) are associated with point mutations in the alternative splicing regulator RBM20 that are frequently located in the arginine/serine-rich domain (RS-domain). Such mutations can cause defective splicing and cytoplasmic mislocalization, which leads to the formation of detrimental cytoplasmic granules. Successful development of personalized therapies requires identifying the direct mechanisms of pathogenic RBM20 variants. Here, we decipher the molecular mechanism of RBM20 mislocalization and its specific role in DCM pathogenesis. We demonstrate that mislocalized RBM20 RS-domain variants retain their splice regulatory activity, which reveals that aberrant cellular localization is the main driver of their pathological phenotype. A genome-wide CRISPR knockout screen combined with image-enabled cell sorting identified Transportin-3 (TNPO3) as the main nuclear importer of RBM20. We show that the direct RBM20-TNPO3 interaction involves the RS-domain, and is disrupted by pathogenic variants. Relocalization of pathogenic RBM20 variants to the nucleus restores alternative splicing and dissolves cytoplasmic granules in cell culture and animal models. These findings provide proof-of-principle for developing therapeutic strategies to restore RBM20's nuclear localization in RBM20-DCM patients.
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Affiliation(s)
- Julia Kornienko
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany
| | | | - Kai Fenzl
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany
| | - Florian Hinze
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Schraivogel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Markus Grosch
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brigit Tunaj
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Dominik Lindenhofer
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Laura Schraft
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Moritz Kueblbeck
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Eric Smith
- University of Michigan, Ann Arbor, MI, USA
| | - Chad Mao
- Children's Healthcare of Atlanta & Emory University, Atlanta, GA, USA
| | | | - Anjali Owens
- University of Pennsylvania, Philadelphia, PA, USA
| | - Ardan M Saguner
- Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, Zurich, Switzerland
| | - Benjamin Meder
- Cardiogenetics Center Heidelberg, Department of Cardiology, Angiology and Pulmology, University Hospital Heidelberg, Heidelberg, Germany
| | - Victoria Parikh
- Stanford Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Gotthardt
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lars M Steinmetz
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Genome Technology Center, Palo Alto, CA, USA.
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41
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Sansbury SE, Serebrenik YV, Lapidot T, Burslem GM, Shalem O. Pooled tagging and hydrophobic targeting of endogenous proteins for unbiased mapping of unfolded protein responses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548611. [PMID: 37503003 PMCID: PMC10370017 DOI: 10.1101/2023.07.13.548611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
System-level understanding of proteome organization and function requires methods for direct visualization and manipulation of proteins at scale. We developed an approach enabled by high-throughput gene tagging for the generation and analysis of complex cell pools with endogenously tagged proteins. Proteins are tagged with HaloTag to enable visualization or direct perturbation. Fluorescent labeling followed by in situ sequencing and deep learning-based image analysis identifies the localization pattern of each tag, providing a bird's-eye-view of cellular organization. Next, we use a hydrophobic HaloTag ligand to misfold tagged proteins, inducing spatially restricted proteotoxic stress that is read out by single cell RNA sequencing. By integrating optical and perturbation data, we map compartment-specific responses to protein misfolding, revealing inter-compartment organization and direct crosstalk, and assigning proteostasis functions to uncharacterized genes. Altogether, we present a powerful and efficient method for large-scale studies of proteome dynamics, function, and homeostasis.
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42
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She R, Fair T, Schaefer NK, Saunders RA, Pavlovic BJ, Weissman JS, Pollen AA. Comparative landscape of genetic dependencies in human and chimpanzee stem cells. Cell 2023; 186:2977-2994.e23. [PMID: 37343560 PMCID: PMC10461406 DOI: 10.1016/j.cell.2023.05.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/14/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023]
Abstract
Comparative studies of great apes provide a window into our evolutionary past, but the extent and identity of cellular differences that emerged during hominin evolution remain largely unexplored. We established a comparative loss-of-function approach to evaluate whether human cells exhibit distinct genetic dependencies. By performing genome-wide CRISPR interference screens in human and chimpanzee pluripotent stem cells, we identified 75 genes with species-specific effects on cellular proliferation. These genes comprised coherent processes, including cell-cycle progression and lysosomal signaling, which we determined to be human-derived by comparison with orangutan cells. Human-specific robustness to CDK2 and CCNE1 depletion persisted in neural progenitor cells and cerebral organoids, supporting the G1-phase length hypothesis as a potential evolutionary mechanism in human brain expansion. Our findings demonstrate that evolutionary changes in human cells reshaped the landscape of essential genes and establish a platform for systematically uncovering latent cellular and molecular differences between species.
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Affiliation(s)
- Richard She
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Tyler Fair
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Nathan K Schaefer
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Reuben A Saunders
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Department of Cellular and Molecular Pharmacology, University of California at San Francisco, San Francisco, CA, USA
| | - Bryan J Pavlovic
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute Technology, Cambridge, MA 02142, USA.
| | - Alex A Pollen
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
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43
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Tang YJ, Shuldiner EG, Karmakar S, Winslow MM. High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo. Cold Spring Harb Perspect Med 2023; 13:a041382. [PMID: 37277208 PMCID: PMC10317066 DOI: 10.1101/cshperspect.a041382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The vast number of genomic and molecular alterations in cancer pose a substantial challenge to uncovering the mechanisms of tumorigenesis and identifying therapeutic targets. High-throughput functional genomic methods in genetically engineered mouse models allow for rapid and systematic investigation of cancer driver genes. In this review, we discuss the basic concepts and tools for multiplexed investigation of functionally important cancer genes in vivo using autochthonous cancer models. Furthermore, we highlight emerging technical advances in the field, potential opportunities for future investigation, and outline a vision for integrating multiplexed genetic perturbations with detailed molecular analyses to advance our understanding of the genetic and molecular basis of cancer.
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Affiliation(s)
- Yuning J Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Emily G Shuldiner
- Department of Biology, Stanford University, Stanford, California 94305, USA
| | - Saswati Karmakar
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Monte M Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
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44
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Tian H, Davis HC, Wong-Campos JD, Park P, Fan LZ, Gmeiner B, Begum S, Werley CA, Borja GB, Upadhyay H, Shah H, Jacques J, Qi Y, Parot V, Deisseroth K, Cohen AE. Video-based pooled screening yields improved far-red genetically encoded voltage indicators. Nat Methods 2023; 20:1082-1094. [PMID: 36624211 PMCID: PMC10329731 DOI: 10.1038/s41592-022-01743-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 11/28/2022] [Indexed: 01/11/2023]
Abstract
Video-based screening of pooled libraries is a powerful approach for directed evolution of biosensors because it enables selection along multiple dimensions simultaneously from large libraries. Here we develop a screening platform, Photopick, which achieves precise phenotype-activated photoselection over a large field of view (2.3 × 2.3 mm, containing >103 cells, per shot). We used the Photopick platform to evolve archaerhodopsin-derived genetically encoded voltage indicators (GEVIs) with improved signal-to-noise ratio (QuasAr6a) and kinetics (QuasAr6b). These GEVIs gave improved signals in cultured neurons and in live mouse brains. By combining targeted in vivo optogenetic stimulation with high-precision voltage imaging, we characterized inhibitory synaptic coupling between individual cortical NDNF (neuron-derived neurotrophic factor) interneurons, and excitatory electrical synapses between individual hippocampal parvalbumin neurons. The QuasAr6 GEVIs are powerful tools for all-optical electrophysiology and the Photopick approach could be adapted to evolve a broad range of biosensors.
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Affiliation(s)
- He Tian
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Hunter C Davis
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - J David Wong-Campos
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Pojeong Park
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Linlin Z Fan
- Department of Bioengineering, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Benjamin Gmeiner
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Shahinoor Begum
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | | | | | | | | | | | - Yitong Qi
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Vicente Parot
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Karl Deisseroth
- Department of Bioengineering, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MA, USA
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
- Department of Physics, Harvard University, Cambridge, MA, USA.
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45
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Schwartz M, Israel U, Wang XJ, Laubscher E, Yu C, Dilip R, Li Q, Mari J, Soro J, Yu K, Pradhan E, Ates A, Gallandt D, Barnowski R, Pao E, Van Valen D. Scaling biological discovery at the interface of deep learning and cellular imaging. Nat Methods 2023; 20:956-957. [PMID: 37434003 DOI: 10.1038/s41592-023-01931-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Affiliation(s)
- Morgan Schwartz
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Uriah Israel
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Xuefei Julie Wang
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Emily Laubscher
- Department of Chemistry, California Institute of Technology, Pasadena, CA, USA
| | - Changhua Yu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Rohit Dilip
- Department of Computer Science, California Institute of Technology, Pasadena, CA, USA
| | - Qilin Li
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Joud Mari
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Johnathon Soro
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Kevin Yu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Elora Pradhan
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Ada Ates
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Danielle Gallandt
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Ross Barnowski
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Edward Pao
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
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46
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Fei X, Huang J, Li F, Wang Y, Shao Z, Dong L, Wu Y, Li B, Zhang X, Lv B, Zhao Y, Weng Q, Chen K, Zhang M, Yang S, Zhang C, Zhang M, Li W, Ying S, Sun Q, Chen Z, Shen H. The Scap-SREBP1-S1P/S2P lipogenesis signal orchestrates the homeostasis and spatiotemporal activation of NF-κB. Cell Rep 2023; 42:112586. [PMID: 37267109 DOI: 10.1016/j.celrep.2023.112586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/05/2023] [Accepted: 05/16/2023] [Indexed: 06/04/2023] Open
Abstract
The nuclear factor κB (NF-κB) pathway plays essential roles in innate and adaptive immunity, but little is known how NF-κB signaling is compartmentalized and spatiotemporally activated in the cytoplasm. Here, we show that the lipogenesis signal cascade Scap-SREBP1-S1P/S2P orchestrates the homeostasis and spatiotemporal activation of NF-κB. SREBP cleavage-activating protein (Scap) and sterol regulatory element-binding protein 1 (SREBP1) form a super complex with inhibitors of NF-κB α (IκBα) to associate NF-κB close to the endoplasmic reticulum (ER). Upon lipopolysaccharide (LPS) stimulation, Scap transports the complex to the Golgi apparatus, where SREBP1 is cleaved by site-1 protease (S1P)/S2P, liberating IκBα for IκB kinase (Ikk)-mediated phosphorylation and subsequent activation of NF-κB. Loss of Scap or inhibition of S1P or S2P diminishes, while SREBP1 deficiency augments, LPS-induced NF-κB activation and subsequent inflammatory responses. Our results reveal the Scap-SREBP1 complex as an additional cytoplasmic checkpoint for NF-κB homeostasis and unveil the Golgi apparatus as the optimal cellular platform for NF-κB activation, providing insights into the crosstalk between lipogenesis signaling and immunity.
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Affiliation(s)
- Xia Fei
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China; Department of Respiratory and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Jiaqi Huang
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Fei Li
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Yuejue Wang
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Zhehua Shao
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Lingling Dong
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Yinfang Wu
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Boran Li
- Department of Biochemistry, Department of Cardiology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Xue Zhang
- Department of Respiratory and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Baihui Lv
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Yun Zhao
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Qingyu Weng
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Kaijun Chen
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Min Zhang
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Shiyi Yang
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Chao Zhang
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Min Zhang
- Department of Respiratory and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Wen Li
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Songmin Ying
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Qiming Sun
- Department of Biochemistry, Department of Cardiology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China.
| | - Zhihua Chen
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China.
| | - Huahao Shen
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China; State Key Lab for Respiratory Diseases, National Clinical Research Centre for Respiratory Disease, Guangzhou, Guangdong 510120, China.
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47
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Tian L, Chen F, Macosko EZ. The expanding vistas of spatial transcriptomics. Nat Biotechnol 2023; 41:773-782. [PMID: 36192637 PMCID: PMC10091579 DOI: 10.1038/s41587-022-01448-2] [Citation(s) in RCA: 93] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/26/2022] [Indexed: 11/09/2022]
Abstract
The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell-cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics.
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Affiliation(s)
- Luyi Tian
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Stem Cell and Regenerative Biology, Cambridge, MA, USA.
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
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48
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Kalamakis G, Platt RJ. CRISPR for neuroscientists. Neuron 2023:S0896-6273(23)00306-9. [PMID: 37201524 DOI: 10.1016/j.neuron.2023.04.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/14/2023] [Accepted: 04/18/2023] [Indexed: 05/20/2023]
Abstract
Genome engineering technologies provide an entry point into understanding and controlling the function of genetic elements in health and disease. The discovery and development of the microbial defense system CRISPR-Cas yielded a treasure trove of genome engineering technologies and revolutionized the biomedical sciences. Comprising diverse RNA-guided enzymes and effector proteins that evolved or were engineered to manipulate nucleic acids and cellular processes, the CRISPR toolbox provides precise control over biology. Virtually all biological systems are amenable to genome engineering-from cancer cells to the brains of model organisms to human patients-galvanizing research and innovation and giving rise to fundamental insights into health and powerful strategies for detecting and correcting disease. In the field of neuroscience, these tools are being leveraged across a wide range of applications, including engineering traditional and non-traditional transgenic animal models, modeling disease, testing genomic therapies, unbiased screening, programming cell states, and recording cellular lineages and other biological processes. In this primer, we describe the development and applications of CRISPR technologies while highlighting outstanding limitations and opportunities.
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Affiliation(s)
- Georgios Kalamakis
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland; Novartis Institutes for BioMedical Research, 4056 Basel, Switzerland
| | - Randall J Platt
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland; Department of Chemistry, University of Basel, Petersplatz 1, 4003 Basel, Switzerland; NCCR MSE, Mattenstrasse 24a, 4058 Basel, Switzerland; Botnar Research Center for Child Health, Mattenstrasse 24a, 4058 Basel, Switzerland.
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49
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Pan C, Qi Y. CRISPR-Combo-mediated orthogonal genome editing and transcriptional activation for plant breeding. Nat Protoc 2023:10.1038/s41596-023-00823-w. [PMID: 37085666 DOI: 10.1038/s41596-023-00823-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 02/09/2023] [Indexed: 04/23/2023]
Abstract
CRISPR-Cas nuclease systems, base editors, and CRISPR activation have greatly advanced plant genome engineering. However, the combinatorial approaches for multiplexed orthogonal genome editing and transcriptional regulation were previously unexploited in plants. We have recently established a single Cas9 protein-based CRISPR-Combo platform, enabling efficient multiplexed orthogonal genome editing (double-strand break-mediated genome editing or base editing) and transcriptional activation in plants via engineering the single guide RNA (sgRNA) structure. Here, we provide step-by-step instructions for constructing CRISPR-Combo systems for speed breeding of transgene-free, genome-edited Arabidopsis plants and enhancing rice regeneration with more heritable targeted mutations in a hormone-free manner. We also provide guidance on designing efficient sgRNA, Agrobacterium-mediated transformation of Arabidopsis and rice, rice regeneration without exogenous plant hormones, gene editing evaluation and visual identification of transgene-free Arabidopsis plants with high editing activity. With the use of this protocol, it takes ~2 weeks to establish the CRISPR-Combo systems, 4 months to obtain transgene-free genome-edited Arabidopsis plants and 4 months to obtain rice plants with enrichment of heritable targeted mutations by hormone-free tissue culture.
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Affiliation(s)
- Changtian Pan
- Department of Horticulture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, USA.
| | - Yiping Qi
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, USA.
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, USA.
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50
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Lutz ID, Wang S, Norn C, Courbet A, Borst AJ, Zhao YT, Dosey A, Cao L, Xu J, Leaf EM, Treichel C, Litvicov P, Li Z, Goodson AD, Rivera-Sánchez P, Bratovianu AM, Baek M, King NP, Ruohola-Baker H, Baker D. Top-down design of protein architectures with reinforcement learning. Science 2023; 380:266-273. [PMID: 37079676 DOI: 10.1126/science.adf6591] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 03/21/2023] [Indexed: 04/22/2023]
Abstract
As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a "top-down" reinforcement learning-based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo-electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.
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Affiliation(s)
- Isaac D Lutz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Shunzhi Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Christoffer Norn
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
| | - Alexis Courbet
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Andrew J Borst
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Yan Ting Zhao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Oral Health Sciences, University of Washington, Seattle, WA, USA
| | - Annie Dosey
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Jinwei Xu
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Elizabeth M Leaf
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Catherine Treichel
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Patrisia Litvicov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Zhe Li
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alexander D Goodson
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | | | - Minkyung Baek
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Neil P King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hannele Ruohola-Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Oral Health Sciences, University of Washington, Seattle, WA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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