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Elmalam N, Ben Nedava L, Zaritsky A. In silico labeling in cell biology: Potential and limitations. Curr Opin Cell Biol 2024; 89:102378. [PMID: 38838549 DOI: 10.1016/j.ceb.2024.102378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/07/2024]
Abstract
In silico labeling is the computational cross-modality image translation where the output modality is a subcellular marker that is not specifically encoded in the input image, for example, in silico localization of organelles from transmitted light images. In principle, in silico labeling has the potential to facilitate rapid live imaging of multiple organelles with reduced photobleaching and phototoxicity, a technology enabling a major leap toward understanding the cell as an integrated complex system. However, five years have passed since feasibility was attained, without any demonstration of using in silico labeling to uncover new biological insight. In here, we discuss the current state of in silico labeling, the limitations preventing it from becoming a practical tool, and how we can overcome these limitations to reach its full potential.
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Affiliation(s)
- Nitsan Elmalam
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Lion Ben Nedava
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
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2
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Bojmar L, Zambirinis CP, Hernandez JM, Chakraborty J, Shaashua L, Kim J, Johnson KE, Hanna S, Askan G, Burman J, Ravichandran H, Zheng J, Jolissaint JS, Srouji R, Song Y, Choubey A, Kim HS, Cioffi M, van Beek E, Sigel C, Jessurun J, Velasco Riestra P, Blomstrand H, Jönsson C, Jönsson A, Lauritzen P, Buehring W, Ararso Y, Hernandez D, Vinagolu-Baur JP, Friedman M, Glidden C, Firmenich L, Lieberman G, Mejia DL, Nasar N, Mutvei AP, Paul DM, Bram Y, Costa-Silva B, Basturk O, Boudreau N, Zhang H, Matei IR, Hoshino A, Kelsen D, Sagi I, Scherz A, Scherz-Shouval R, Yarden Y, Oren M, Egeblad M, Lewis JS, Keshari K, Grandgenett PM, Hollingsworth MA, Rajasekhar VK, Healey JH, Björnsson B, Simeone DM, Tuveson DA, Iacobuzio-Donahue CA, Bromberg J, Vincent CT, O'Reilly EM, DeMatteo RP, Balachandran VP, D'Angelica MI, Kingham TP, Allen PJ, Simpson AL, Elemento O, Sandström P, Schwartz RE, Jarnagin WR, Lyden D. Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer. Nat Med 2024; 30:2170-2180. [PMID: 38942992 DOI: 10.1038/s41591-024-03075-7] [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: 10/10/2023] [Accepted: 05/17/2024] [Indexed: 06/30/2024]
Abstract
Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1 and NR1D1) and an increase of CD11B+ natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.
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Affiliation(s)
- Linda Bojmar
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Constantinos P Zambirinis
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Division of Surgical Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Jonathan M Hernandez
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Thoracic and Gastrointestinal Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jayasree Chakraborty
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lee Shaashua
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Junbum Kim
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Kofi Ennu Johnson
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Samer Hanna
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Gokce Askan
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jonas Burman
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Hiranmayi Ravichandran
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jian Zheng
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joshua S Jolissaint
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Srouji
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yi Song
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ankur Choubey
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Han Sang Kim
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Michele Cioffi
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Elke van Beek
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Carlie Sigel
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jose Jessurun
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | - Hakon Blomstrand
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Department of Clinical Pathology, Linköping University, Linköping, Sweden
| | - Carolin Jönsson
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Anette Jönsson
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Pernille Lauritzen
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Weston Buehring
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Yonathan Ararso
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Dylanne Hernandez
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Jessica P Vinagolu-Baur
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Madison Friedman
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Caroline Glidden
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Laetitia Firmenich
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Grace Lieberman
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Dianna L Mejia
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Naaz Nasar
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anders P Mutvei
- Department of Laboratory Medicine, Division of Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Doru M Paul
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Yaron Bram
- Division of Gastroenterology & Hepatology, Weill Cornell Medicine, New York, NY, USA
| | - Bruno Costa-Silva
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Olca Basturk
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nancy Boudreau
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Haiying Zhang
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Irina R Matei
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - Ayuko Hoshino
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA
| | - David Kelsen
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Irit Sagi
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Avigdor Scherz
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Ruth Scherz-Shouval
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Yosef Yarden
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Moshe Oren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Mikala Egeblad
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jason S Lewis
- Radiology and Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Kayvan Keshari
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Radiology and Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Paul M Grandgenett
- Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Michael A Hollingsworth
- Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Vinagolu K Rajasekhar
- Orthopedic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John H Healey
- Orthopedic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bergthor Björnsson
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Diane M Simeone
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
| | | | - Christine A Iacobuzio-Donahue
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jaqueline Bromberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - C Theresa Vincent
- Department of Laboratory Medicine, Division of Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Eileen M O'Reilly
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Ronald P DeMatteo
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Vinod P Balachandran
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael I D'Angelica
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - T Peter Kingham
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter J Allen
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Olivier Elemento
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Per Sandström
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Robert E Schwartz
- Division of Gastroenterology & Hepatology, Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - William R Jarnagin
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David Lyden
- Departments of Pediatrics and Cell and Developmental Biology, Children's Cancer and Blood Foundation Laboratories, Drukier Institute for Children's Health, Meyer Cancer Center Weill Cornell Medicine, New York, NY, USA.
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Wang Y, Zhao J, Xu H, Han C, Tao Z, Zhou D, Geng T, Liu D, Ji Z. A systematic evaluation of computational methods for cell segmentation. Brief Bioinform 2024; 25:bbae407. [PMID: 39154193 PMCID: PMC11330341 DOI: 10.1093/bib/bbae407] [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: 03/27/2024] [Revised: 06/28/2024] [Accepted: 08/01/2024] [Indexed: 08/19/2024] Open
Abstract
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.
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Affiliation(s)
- Yuxing Wang
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, United States
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States
| | - Junhan Zhao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Hongye Xu
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, United States
| | - Cheng Han
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, United States
| | - Zhiqiang Tao
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, United States
| | - Dawei Zhou
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Tong Geng
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
| | - Dongfang Liu
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, United States
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States
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Di Mauro F, Arbore G. Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine. Cancer Immunol Res 2024; 12:800-813. [PMID: 38657223 PMCID: PMC11217735 DOI: 10.1158/2326-6066.cir-23-0699] [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: 08/28/2023] [Revised: 01/12/2024] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.
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Affiliation(s)
- Francesco Di Mauro
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Giuseppina Arbore
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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5
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Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 2024; 21:1153-1165. [PMID: 38997593 DOI: 10.1038/s41592-024-02327-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 05/28/2024] [Indexed: 07/14/2024]
Abstract
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
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Affiliation(s)
- Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Deep Piction GmbH, Munich, Germany.
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6
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Yurchenko A, Özkul G, van Riel NAW, van Hest JCM, de Greef TFA. Mechanism-based and data-driven modeling in cell-free synthetic biology. Chem Commun (Camb) 2024; 60:6466-6475. [PMID: 38847387 DOI: 10.1039/d4cc01289e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.
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Affiliation(s)
- Angelina Yurchenko
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Gökçe Özkul
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Natal A W van Riel
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Eindhoven MedTech Innovation Center, 5612 AX Eindhoven, The Netherlands
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jan C M van Hest
- Bio-Organic Chemistry, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
- Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
- Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, 3584 CB Utrecht, The Netherlands
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7
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Bennett JJR, Stern AD, Zhang X, Birtwistle MR, Pandey G. Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events. NPJ Syst Biol Appl 2024; 10:65. [PMID: 38834572 PMCID: PMC11150372 DOI: 10.1038/s41540-024-00389-7] [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: 01/29/2024] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.
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Affiliation(s)
- Jamie J R Bennett
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alan D Stern
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiang Zhang
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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8
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Shroff H, Testa I, Jug F, Manley S. Live-cell imaging powered by computation. Nat Rev Mol Cell Biol 2024; 25:443-463. [PMID: 38378991 DOI: 10.1038/s41580-024-00702-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/22/2024]
Abstract
The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.
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Affiliation(s)
- Hari Shroff
- Janelia Research Campus, Howard Hughes Medical Institute (HHMI), Ashburn, VA, USA
| | - Ilaria Testa
- Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Florian Jug
- Fondazione Human Technopole (HT), Milan, Italy
| | - Suliana Manley
- Institute of Physics, School of Basic Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
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9
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Alahmari SS, Goldgof D, Hall LO, Mouton PR. A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7458-7477. [PMID: 36327184 DOI: 10.1109/tnnls.2022.3213407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions for medical image analysis. Furthermore, accurate stereological quantification of microscopic structures in stained tissue sections plays a critical role in understanding human diseases and developing safe and effective treatments. In this article, we review the most recent deep learning approaches for cell (nuclei) detection and segmentation in cancer and Alzheimer's disease with an emphasis on deep learning approaches combined with unbiased stereology. Major challenges include accurate and reproducible cell detection and segmentation of microscopic images from stained sections. Finally, we discuss potential improvements and future trends in deep learning applied to cell detection and segmentation.
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10
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Rahman MA, Yan F, Li R, Wang Y, Huang L, Han R, Jiang Y. Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells. Pharmaceutics 2024; 16:673. [PMID: 38794335 PMCID: PMC11125085 DOI: 10.3390/pharmaceutics16050673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy.
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Affiliation(s)
- Md. Atiqur Rahman
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Feihong Yan
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruiyuan Li
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Wang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lu Huang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rongcheng Han
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuqiang Jiang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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11
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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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12
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Vercammen H, Ondra M, Kotulova J, De La Hoz EC, Witters C, Jecmenova K, Le Compte M, Deben C, Ní Dhubhghaill S, Koppen C, Hajdúch M, Van den Bogerd B. "Keep on ROCKIn": Repurposed ROCK inhibitors to boost corneal endothelial regeneration. Biomed Pharmacother 2024; 174:116435. [PMID: 38513591 DOI: 10.1016/j.biopha.2024.116435] [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: 01/10/2024] [Revised: 03/06/2024] [Accepted: 03/15/2024] [Indexed: 03/23/2024] Open
Abstract
The global shortage of corneal endothelial graft tissue necessitates the exploration of alternative therapeutic strategies. Rho-associated protein kinase inhibitors (ROCKi), recognized for their regenerative potential in cardiology, oncology, and neurology, have shown promise in corneal endothelial regeneration. This study investigates the repurposing potential of additional ROCKi compounds. Through screening a self-assembled library of ROCKi on B4G12 corneal endothelial cells, we evaluated their dose-dependent effects on proliferation, migration, and toxicity using live-cell imaging. Nine ROCKi candidates significantly enhanced B4G12 proliferation compared to the basal growth rate. These candidates were further assessed for their potential to accelerate wound closure as another indicator for tissue regeneration capacity, with most demonstrating notable efficacy. To assess the potential impact of candidate ROCKi on key corneal endothelial cell markers related to cell proliferation, leaky tight junctions and ion efflux capacity, we analyzed the protein expression of cyclin E1, CDK2, p16, ZO-1 and Na+/K+-ATPase, respectively. Immunocytochemistry and western blot analysis confirmed the preservation of corneal endothelial markers post-treatment with ROCKi hits. However, notable cytoplasm enlargement and nuclear fragmentation were detected after the treatment with SR-3677 and Thiazovivin, indicating possible cellular stress. In compared parameters, Chroman-1 at a concentration of 10 nM outperformed other ROCKi, requiring significantly 1000-fold lower effective concentration than established ROCKi Y-27632 and Fasudil. Altogether, this study underscores the potential of repurposing ROCKi for treating corneal endothelial dysfunctions, offering a viable alternative to conventional grafting methods, and highlights Chroman-1 as a promising candidate structure for hit-to-lead development.
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Affiliation(s)
- Hendrik Vercammen
- Antwerp Research Group for Ocular Science (ARGOS), Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium; Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium; DrugVision Lab, University of Antwerp, Wilrijk, Belgium.
| | - Martin Ondra
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic; Czech Advanced Technology and Research Institute (CATRIN), Palacky University Olomouc, Olomouc, Czech Republic
| | - Jana Kotulova
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | | | - Charissa Witters
- Antwerp Research Group for Ocular Science (ARGOS), Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium; Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium; DrugVision Lab, University of Antwerp, Wilrijk, Belgium
| | - Katerina Jecmenova
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | | | | | | | - Carina Koppen
- Antwerp Research Group for Ocular Science (ARGOS), Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium; Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium
| | - Marián Hajdúch
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic; Czech Advanced Technology and Research Institute (CATRIN), Palacky University Olomouc, Olomouc, Czech Republic
| | - Bert Van den Bogerd
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic; Czech Advanced Technology and Research Institute (CATRIN), Palacky University Olomouc, Olomouc, Czech Republic.
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13
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Hernandez-Beltran JCR, Rodríguez-Beltrán J, Aguilar-Luviano OB, Velez-Santiago J, Mondragón-Palomino O, MacLean RC, Fuentes-Hernández A, San Millán A, Peña-Miller R. Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria. Nat Commun 2024; 15:2610. [PMID: 38521779 PMCID: PMC10960800 DOI: 10.1038/s41467-024-45045-0] [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: 04/24/2023] [Accepted: 01/12/2024] [Indexed: 03/25/2024] Open
Abstract
The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.
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Affiliation(s)
- J Carlos R Hernandez-Beltran
- Center for Genomic Sciences, Universidad Nacional Autónoma de México, 62210, Cuernavaca, México.
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, 24306, Plön, Germany.
| | | | | | - Jesús Velez-Santiago
- Center for Genomic Sciences, Universidad Nacional Autónoma de México, 62210, Cuernavaca, México
| | - Octavio Mondragón-Palomino
- Laboratory of Parasitic Diseases, National Institute for Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - R Craig MacLean
- Department of Biology, University of Oxford, OX1 3SZ, Oxford, UK
| | - Ayari Fuentes-Hernández
- Center for Genomic Sciences, Universidad Nacional Autónoma de México, 62210, Cuernavaca, México
| | - Alvaro San Millán
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología - CSIC, 28049, Madrid, Spain
| | - Rafael Peña-Miller
- Center for Genomic Sciences, Universidad Nacional Autónoma de México, 62210, Cuernavaca, México.
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14
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Ding J, Liu R, Wen H, Tang W, Li Z, Venegas J, Su R, Molho D, Jin W, Wang Y, Lu Q, Li L, Zuo W, Chang Y, Xie Y, Tang J. DANCE: a deep learning library and benchmark platform for single-cell analysis. Genome Biol 2024; 25:72. [PMID: 38504331 PMCID: PMC10949782 DOI: 10.1186/s13059-024-03211-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: 02/07/2023] [Accepted: 03/05/2024] [Indexed: 03/21/2024] Open
Abstract
DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
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Affiliation(s)
- Jiayuan Ding
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Hongzhi Wen
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA
| | - Wenzhuo Tang
- Department of Statistics and Probability, Michigan State University, East Lansing, USA
| | - Zhaoheng Li
- Department of Biostatistics, University of Washington, Seattle, USA
| | - Julian Venegas
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Runze Su
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, USA
| | - Dylan Molho
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Wei Jin
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, USA
| | - Qiaolin Lu
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Lingxiao Li
- Department of Computer Science, Boston University, Boston, USA
| | - Wangyang Zuo
- Department of Computer Science, Zhejiang University of Technology, Zhejiang, China
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Yuying Xie
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
- Department of Statistics and Probability, Michigan State University, East Lansing, USA.
| | - Jiliang Tang
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
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15
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Ohara K, Rendeiro AF, Bhinder B, Eng KW, Ravichandran H, Nguyen D, Pisapia D, Vosoughi A, Fernandez E, Shohdy KS, Manohar J, Beg S, Wilkes D, Robinson BD, Khani F, Bareja R, Tagawa ST, Ouseph MM, Sboner A, Elemento O, Faltas BM, Mosquera JM. The evolution of metastatic upper tract urothelial carcinoma through genomic-transcriptomic and single-cell protein markers analysis. Nat Commun 2024; 15:2009. [PMID: 38499531 PMCID: PMC10948878 DOI: 10.1038/s41467-024-46320-w] [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: 12/26/2021] [Accepted: 02/22/2024] [Indexed: 03/20/2024] Open
Abstract
The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are not well understood, and there is a lack of knowledge regarding the genomic and transcriptomic differences between primary and metastatic UTUC. To address these gaps, we integrate whole-exome sequencing, RNA sequencing, and Imaging Mass Cytometry using lanthanide metal-conjugated antibodies of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. We perform a spatially-resolved single-cell analysis of cancer, immune, and stromal cells to understand the evolution of primary to metastatic UTUC. We discover that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature are stable across primary and matched metastatic UTUC. Molecular and immune subtypes are consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single cells. Molecular subtypes at the single-cell level are highly conserved between primary and metastatic UTUC tumors within the same patient.
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Affiliation(s)
- Kentaro Ohara
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - André Figueiredo Rendeiro
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Avenue, New York, NY, 10065, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14 AKH BT 25.3, 1090, Vienna, Austria
| | - Bhavneet Bhinder
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Avenue, New York, NY, 10065, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
| | - Kenneth Wha Eng
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
| | - Hiranmayi Ravichandran
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Duy Nguyen
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - David Pisapia
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Aram Vosoughi
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Evan Fernandez
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
| | - Kyrillus S Shohdy
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Jyothi Manohar
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Shaham Beg
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - David Wilkes
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Brian D Robinson
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Francesca Khani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Rohan Bareja
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Avenue, New York, NY, 10065, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
| | - Scott T Tagawa
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, 10065, USA
- Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine, New York, NY, 10065, USA
| | - Madhu M Ouseph
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Andrea Sboner
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
| | - Olivier Elemento
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Avenue, New York, NY, 10065, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
- Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine, New York, NY, 10065, USA
| | - Bishoy M Faltas
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA.
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, 10065, USA.
- Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine, New York, NY, 10065, USA.
- Departments of Cell and Developmental Biology, Weill Cornell Medicine, New York, NY, 10065, USA.
| | - Juan Miguel Mosquera
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA.
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA.
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16
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Alsup AM, Fowlds K, Cho M, Luber JM. BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells. PLoS One 2024; 19:e0299549. [PMID: 38489336 PMCID: PMC10942061 DOI: 10.1371/journal.pone.0299549] [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: 04/10/2023] [Accepted: 02/12/2024] [Indexed: 03/17/2024] Open
Abstract
Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers-all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions.
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Affiliation(s)
- Anne M. Alsup
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States of America
| | - Kelli Fowlds
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States of America
| | - Michael Cho
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States of America
| | - Jacob M. Luber
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States of America
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States of America
- Multi-Interprofessional Center for Health Informatics, University of Texas at Arlington, Arlington, TX, United States of America
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17
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Yeo YY, Cramer P, Deisher A, Bai Y, Zhu B, Yeo WJ, Shipp MA, Rodig SJ, Jiang S. A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging. Methods Cell Biol 2024; 186:213-231. [PMID: 38705600 PMCID: PMC11244641 DOI: 10.1016/bs.mcb.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section. Here, we describe an optimized MIBI workflow for high-plex analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues following antigen retrieval, metal isotope-conjugated antibody staining, imaging using the MIBI instrument, and subsequent data processing and analysis. While this workflow is focused on imaging human FFPE samples using the MIBI, this workflow can be easily extended to model systems, biological questions, and multiplexed imaging modalities.
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Affiliation(s)
- Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, United States
| | - Precious Cramer
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Addison Deisher
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yunhao Bai
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, United States
| | - Bokai Zhu
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, United States
| | - Wan-Jin Yeo
- Department of Physics, Institute of Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Margaret A Shipp
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, United States; Department of Pathology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
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18
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Yeo YY, Qiu H, Bai Y, Zhu B, Chang Y, Yeung J, Michel HA, Wright K, Shaban M, Sadigh S, Nkosi D, Shanmugam V, Rock P, Tung Yiu SP, Cramer P, Paczkowska J, Stephan P, Liao G, Huang AY, Wang H, Chen H, Frauenfeld L, Mitra B, Gewurz BE, Schürch CM, Zhao B, Nolan GP, Zhang B, Shalek AK, Angelo M, Mahmood F, Ma Q, Burack WR, Shipp MA, Rodig SJ, Jiang S. Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583586. [PMID: 38496566 PMCID: PMC10942289 DOI: 10.1101/2024.03.05.583586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.
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19
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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20
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Trettner KJ, Hsieh J, Xiao W, Lee JSH, Armani AM. Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation. APL Bioeng 2024; 8:016121. [PMID: 38566822 PMCID: PMC10985731 DOI: 10.1063/5.0189222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.
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Affiliation(s)
| | - Jeremy Hsieh
- Pasadena Polytechnic High School, Pasadena, California 91106, USA
| | - Weikun Xiao
- Ellison Institute of Technology, Los Angeles, California 90064, USA
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21
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Liu P, Li J, Chang J, Hu P, Sun Y, Jiang Y, Zhang F, Shao H. Software Tools for 2D Cell Segmentation. Cells 2024; 13:352. [PMID: 38391965 PMCID: PMC10886800 DOI: 10.3390/cells13040352] [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: 12/09/2023] [Revised: 01/29/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024] Open
Abstract
Cell segmentation is an important task in the field of image processing, widely used in the life sciences and medical fields. Traditional methods are mainly based on pixel intensity and spatial relationships, but have limitations. In recent years, machine learning and deep learning methods have been widely used, providing more-accurate and efficient solutions for cell segmentation. The effort to develop efficient and accurate segmentation software tools has been one of the major focal points in the field of cell segmentation for years. However, each software tool has unique characteristics and adaptations, and no universal cell-segmentation software can achieve perfect results. In this review, we used three publicly available datasets containing multiple 2D cell-imaging modalities. Common segmentation metrics were used to evaluate the performance of eight segmentation tools to compare their generality and, thus, find the best-performing tool.
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Affiliation(s)
- Ping Liu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China; (P.L.); (J.L.); (J.C.)
| | - Jun Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China; (P.L.); (J.L.); (J.C.)
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 7, Pengfei Road, Dapeng District, Shenzhen 518120, China; (P.H.); (Y.S.); (Y.J.); (F.Z.)
| | - Jiaxing Chang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China; (P.L.); (J.L.); (J.C.)
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 7, Pengfei Road, Dapeng District, Shenzhen 518120, China; (P.H.); (Y.S.); (Y.J.); (F.Z.)
| | - Pinli Hu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 7, Pengfei Road, Dapeng District, Shenzhen 518120, China; (P.H.); (Y.S.); (Y.J.); (F.Z.)
| | - Yue Sun
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 7, Pengfei Road, Dapeng District, Shenzhen 518120, China; (P.H.); (Y.S.); (Y.J.); (F.Z.)
| | - Yanan Jiang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 7, Pengfei Road, Dapeng District, Shenzhen 518120, China; (P.H.); (Y.S.); (Y.J.); (F.Z.)
| | - Fan Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 7, Pengfei Road, Dapeng District, Shenzhen 518120, China; (P.H.); (Y.S.); (Y.J.); (F.Z.)
| | - Haojing Shao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No 7, Pengfei Road, Dapeng District, Shenzhen 518120, China; (P.H.); (Y.S.); (Y.J.); (F.Z.)
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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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Jan M, Spangaro A, Lenartowicz M, Mattiazzi Usaj M. From pixels to insights: Machine learning and deep learning for bioimage analysis. Bioessays 2024; 46:e2300114. [PMID: 38058114 DOI: 10.1002/bies.202300114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 12/08/2023]
Abstract
Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.
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Affiliation(s)
- Mahta Jan
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Allie Spangaro
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Michelle Lenartowicz
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Mojca Mattiazzi Usaj
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
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24
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Wang Y, Zhao J, Xu H, Han C, Tao Z, Zhao D, Zhou D, Tong G, Liu D, Ji Z. A systematic evaluation of computation methods for cell segmentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.28.577670. [PMID: 38352578 PMCID: PMC10862744 DOI: 10.1101/2024.01.28.577670] [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: 02/22/2024]
Abstract
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.
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Affiliation(s)
- Yuxing Wang
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Junhan Zhao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H.Chan School of Public Health, Boston, MA, USA
| | - Hongye Xu
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Cheng Han
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Zhiqiang Tao
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Dongfang Zhao
- Department of Computer Science & eScience Institute, University of Washington, Seattle, WA, USA
| | - Dawei Zhou
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Gang Tong
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | - Dongfang Liu
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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25
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Shaban M, Bai Y, Qiu H, Mao S, Yeung J, Yeo YY, Shanmugam V, Chen H, Zhu B, Weirather JL, Nolan GP, Shipp MA, Rodig SJ, Jiang S, Mahmood F. MAPS: pathologist-level cell type annotation from tissue images through machine learning. Nat Commun 2024; 15:28. [PMID: 38167832 PMCID: PMC10761896 DOI: 10.1038/s41467-023-44188-w] [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: 05/15/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
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Affiliation(s)
- Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Huaying Qiu
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jason L Weirather
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Center for Immuno-oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Sizun Jiang
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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26
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Bejarano DA, Schlitzer A. Unveiling Macrophage Heterogeneity and Their Spatial Distribution Using Multiplexed Tissue Imaging. Methods Mol Biol 2024; 2713:281-296. [PMID: 37639130 DOI: 10.1007/978-1-0716-3437-0_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Macrophages display a high degree of phenotypic diversity and plasticity, which is influenced by their location within the tissue microenvironment. Co-Detection by Indexing (CODEX), a multiplexed imaging technique, allows the simultaneous detection of multiple membrane and cellular markers that enable the accurate identification of tissue-resident hematopoietic and non-hematopoietic cells, while conferring spatial information at a single-cell level. Here we describe the use of CODEX to visualize the phenotypic and spatial heterogeneity of murine tissue-resident macrophages in several organs, and a pipeline to characterize their cellular microenvironments and interactions.
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Affiliation(s)
| | - Andreas Schlitzer
- Quantitative Systems Biology, LIMES Institute, University of Bonn, Bonn, Germany.
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27
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Ram A, Murphy D, DeCuzzi N, Patankar M, Hu J, Pargett M, Albeck JG. A guide to ERK dynamics, part 1: mechanisms and models. Biochem J 2023; 480:1887-1907. [PMID: 38038974 PMCID: PMC10754288 DOI: 10.1042/bcj20230276] [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: 07/09/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 12/02/2023]
Abstract
Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.
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Affiliation(s)
- Abhineet Ram
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Devan Murphy
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Nicholaus DeCuzzi
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Madhura Patankar
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Jason Hu
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - Michael Pargett
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
| | - John G. Albeck
- Department of Molecular and Cellular Biology, University of California, Davis, U.S.A
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28
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Wang Z, Zhang Q, Wang Y, Zhu M, Li Q. A framework for immunofluorescence image augmentation and classification based on unsupervised attention mechanism. JOURNAL OF BIOPHOTONICS 2023; 16:e202300209. [PMID: 37559356 DOI: 10.1002/jbio.202300209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/16/2023] [Accepted: 08/07/2023] [Indexed: 08/11/2023]
Abstract
Autoimmune encephalitis (AE) is a common neurological disorder. As a standard method for neuroautoantibody detection, pathologists use tissue matrix assays (TBA) for initial disease screening. In this study, microscopic fluorescence imaging was combined with deep learning to improve AE diagnostic accuracy. Due to the inter-class imbalance of medical data, we propose an innovative generative adversarial network supplemented with attention mechanisms to highlight key regions in images to synthesize high-quality fluorescence images. However, securing annotated medical data is both time-consuming and costly. To circumvent this problem, we employ a self-supervised learning approach that utilizes unlabeled fluorescence data to support downstream classification tasks. To better understand the fluorescence properties in the data, we introduce a multichannel input convolutional neural network that adds additional channels of fluorescence intensity. This study builds an AE immunofluorescence dataset and obtains the classification accuracy of 88.5% using our method, thus confirming the effectiveness of the proposed method.
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Affiliation(s)
- Ziyi Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
- Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China
| | - Qing Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
- Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
- Engineering Center of SHMEC for Space Information and GNSS, Shanghai, China
| | - Min Zhu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
- Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China
- Engineering Center of SHMEC for Space Information and GNSS, Shanghai, China
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29
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Yu J. Point-Supervised Single-Cell Segmentation via Collaborative Knowledge Sharing. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3884-3894. [PMID: 37676808 DOI: 10.1109/tmi.2023.3312988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the annotation burden. This paper focuses on a weakly-supervised training setting for single-cell segmentation models, where the only available training label is the rough locations of individual cells. The specific problem is of practical interest due to the widely available nuclei counter-stain data in biomedical literature, from which the cell locations can be derived programmatically. Of more general interest is a proposed self-learning method called collaborative knowledge sharing, which is related to but distinct from the more well-known consistency learning methods. This strategy achieves self-learning by sharing knowledge between a principal model and a very light-weight collaborator model. Importantly, the two models are entirely different in their architectures, capacities, and model outputs: In our case, the principal model approaches the segmentation problem from an object-detection perspective, whereas the collaborator model a sematic segmentation perspective. We assessed the effectiveness of this strategy by conducting experiments on LIVECell, a large single-cell segmentation dataset of bright-field images, and on A431 dataset, a fluorescence image dataset in which the location labels are generated automatically from nuclei counter-stain data. Implementing code is available at https://github.com/jiyuuchc/lacss.
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30
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Avtanski D, Hadzi-Petrushev N, Josifovska S, Mladenov M, Reddy V. Emerging technologies in adipose tissue research. Adipocyte 2023; 12:2248673. [PMID: 37599422 PMCID: PMC10443968 DOI: 10.1080/21623945.2023.2248673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 08/03/2023] [Accepted: 08/07/2023] [Indexed: 08/22/2023] Open
Abstract
Technologies are transforming the understanding of adipose tissue as a complex and dynamic tissue that plays a critical role in energy homoeostasis and metabolic health. This mini-review provides a brief overview of the potential impact of novel technologies in biomedical research and aims to identify areas where these technologies can make the most significant contribution to adipose tissue research. It discusses the impact of cutting-edge technologies such as single-cell sequencing, multi-omics analyses, spatial transcriptomics, live imaging, 3D tissue engineering, microbiome analysis, in vivo imaging, and artificial intelligence/machine learning. As these technologies continue to evolve, we can expect them to play an increasingly important role in advancing our understanding of adipose tissue and improving the treatment of related diseases.
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Affiliation(s)
- Dimiter Avtanski
- Friedman Diabetes Institute, Lenox Hill Hospital, New York, NY, USA
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Nikola Hadzi-Petrushev
- Faculty of Natural Sciences and Mathematics, Institute of Biology, “Ss. Cyril and Methodius” University, Skopje, North Macedonia
| | - Slavica Josifovska
- Faculty of Natural Sciences and Mathematics, Institute of Biology, “Ss. Cyril and Methodius” University, Skopje, North Macedonia
| | - Mitko Mladenov
- Faculty of Natural Sciences and Mathematics, Institute of Biology, “Ss. Cyril and Methodius” University, Skopje, North Macedonia
| | - Varun Reddy
- New York Institute of Technology College of Osteopathic Medicine, Old Westbury, NY, USA
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31
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Chai B, Efstathiou C, Yue H, Draviam VM. Opportunities and challenges for deep learning in cell dynamics research. Trends Cell Biol 2023:S0962-8924(23)00228-3. [PMID: 38030542 DOI: 10.1016/j.tcb.2023.10.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/30/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023]
Abstract
The growth of artificial intelligence (AI) has led to an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes but has also started to support advances in drug development, precision medicine, and genome-phenome mapping. We survey existing AI-based techniques and tools, as well as open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from a computational perspective and review emerging research frontiers and innovative applications for DL-guided automation in cell dynamics research.
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Affiliation(s)
- Binghao Chai
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Christoforos Efstathiou
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Haoran Yue
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Viji M Draviam
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK; The Alan Turing Institute, London NW1 2DB, UK.
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32
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Weeratunga P, Denney L, Bull JA, Repapi E, Sergeant M, Etherington R, Vuppussetty C, Turner GDH, Clelland C, Woo J, Cross A, Issa F, de Andrea CE, Melero Bermejo I, Sims D, McGowan S, Zurke YX, Ahern DJ, Gamez EC, Whalley J, Richards D, Klenerman P, Monaco C, Udalova IA, Dong T, Antanaviciute A, Ogg G, Knight JC, Byrne HM, Taylor S, Ho LP. Single cell spatial analysis reveals inflammatory foci of immature neutrophil and CD8 T cells in COVID-19 lungs. Nat Commun 2023; 14:7216. [PMID: 37940670 PMCID: PMC10632491 DOI: 10.1038/s41467-023-42421-0] [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: 01/31/2023] [Accepted: 10/11/2023] [Indexed: 11/10/2023] Open
Abstract
Single cell spatial interrogation of the immune-structural interactions in COVID -19 lungs is challenging, mainly because of the marked cellular infiltrate and architecturally distorted microstructure. To address this, we develop a suite of mathematical tools to search for statistically significant co-locations amongst immune and structural cells identified using 37-plex imaging mass cytometry. This unbiased method reveals a cellular map interleaved with an inflammatory network of immature neutrophils, cytotoxic CD8 T cells, megakaryocytes and monocytes co-located with regenerating alveolar progenitors and endothelium. Of note, a highly active cluster of immature neutrophils and CD8 T cells, is found spatially linked with alveolar progenitor cells, and temporally with the diffuse alveolar damage stage. These findings offer further insights into how immune cells interact in the lungs of severe COVID-19 disease. We provide our pipeline [Spatial Omics Oxford Pipeline (SpOOx)] and visual-analytical tool, Multi-Dimensional Viewer (MDV) software, as a resource for spatial analysis.
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Affiliation(s)
- Praveen Weeratunga
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Laura Denney
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Joshua A Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Emmanouela Repapi
- MRC WIMM Computational Biology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Martin Sergeant
- MRC WIMM Computational Biology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Rachel Etherington
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Chaitanya Vuppussetty
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Gareth D H Turner
- Department of Cellular Pathology and Radcliffe Department of Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Colin Clelland
- Anatomic Pathology, Weill Cornell Medical College, Doha, Qatar
| | - Jeongmin Woo
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Amy Cross
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Fadi Issa
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | | | - David Sims
- MRC WIMM Computational Biology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Simon McGowan
- MRC WIMM Computational Biology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | | | - David J Ahern
- Kennedy Institute for Rheumatology, University of Oxford, Oxford, UK
| | - Eddie C Gamez
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Justin Whalley
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Duncan Richards
- Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Diseases, University of Oxford, Oxford, UK
| | - Paul Klenerman
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Claudia Monaco
- Kennedy Institute for Rheumatology, University of Oxford, Oxford, UK
| | - Irina A Udalova
- Kennedy Institute for Rheumatology, University of Oxford, Oxford, UK
| | - Tao Dong
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK
| | - Agne Antanaviciute
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Graham Ogg
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK
| | - Helen M Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK
| | - Stephen Taylor
- MRC WIMM Computational Biology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Ling-Pei Ho
- MRC Translational Immunology Discovery Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK.
- Respiratory Medicine Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
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33
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Wilson SA, Tank RKJ, Hobbs JK, Foster SJ, Garner EC. An exhaustive multiple knockout approach to understanding cell wall hydrolase function in Bacillus subtilis. mBio 2023; 14:e0176023. [PMID: 37768080 PMCID: PMC10653849 DOI: 10.1128/mbio.01760-23] [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: 07/12/2023] [Accepted: 08/03/2023] [Indexed: 09/29/2023] Open
Abstract
IMPORTANCE In order to grow, bacterial cells must both create and break down their cell wall. The enzymes that are responsible for these processes are the target of some of our best antibiotics. Our understanding of the proteins that break down the wall- cell wall hydrolases-has been limited by redundancy among the large number of hydrolases many bacteria contain. To solve this problem, we identified 42 cell wall hydrolases in Bacillus subtilis and created a strain lacking 40 of them. We show that cells can survive using only a single cell wall hydrolase; this means that to understand the growth of B. subtilis in standard laboratory conditions, it is only necessary to study a very limited number of proteins, simplifying the problem substantially. We additionally show that the ∆40 strain is a research tool to characterize hydrolases, using it to identify three "helper" hydrolases that act in certain stress conditions.
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Affiliation(s)
- Sean A. Wilson
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
- Center for Systems Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Raveen K. J. Tank
- Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom
| | - Jamie K. Hobbs
- Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom
| | - Simon J. Foster
- School of Biosciences, University of Sheffield, Sheffield, United Kingdom
| | - Ethan C. Garner
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
- Center for Systems Biology, Harvard University, Cambridge, Massachusetts, USA
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34
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Wang Y, Wang W, Liu D, Hou W, Zhou T, Ji Z. GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging. Genome Biol 2023; 24:235. [PMID: 37858204 PMCID: PMC10585768 DOI: 10.1186/s13059-023-03054-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 09/08/2023] [Indexed: 10/21/2023] Open
Abstract
When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.
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Affiliation(s)
- Yuxing Wang
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, USA
| | - Wenguan Wang
- The ReLER Lab from the Australian Artificial Intelligence Institute (AAII), University of Technology Sydney, Sydney, Australia
| | - Dongfang Liu
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, USA
| | - Wenpin Hou
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York City, USA
| | - Tianfei Zhou
- Computer Vision Lab, ETH Zurich, Zurich, Switzerland.
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, USA.
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35
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Goyal V, Schaub NJ, Voss TC, Hotaling NA. Unbiased image segmentation assessment toolkit for quantitative differentiation of state-of-the-art algorithms and pipelines. BMC Bioinformatics 2023; 24:388. [PMID: 37828466 PMCID: PMC10568754 DOI: 10.1186/s12859-023-05486-8] [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: 11/22/2022] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Image segmentation pipelines are commonly used in microscopy to identify cellular compartments like nucleus and cytoplasm, but there are few standards for comparing segmentation accuracy across pipelines. The process of selecting a segmentation assessment pipeline can seem daunting to researchers due to the number and variety of metrics available for evaluating segmentation quality. RESULTS Here we present automated pipelines to obtain a comprehensive set of 69 metrics to evaluate segmented data and propose a selection methodology for models based on quantitative analysis, dimension reduction or unsupervised classification techniques and informed selection criteria. CONCLUSION We show that the metrics used here can often be reduced to a small number of metrics that give a more complete understanding of segmentation accuracy, with different groups of metrics providing sensitivity to different types of segmentation error. These tools are delivered as easy to use python libraries, command line tools, Common Workflow Language Tools, and as Web Image Processing Pipeline interactive plugins to ensure a wide range of users can access and use them. We also present how our evaluation methods can be used to observe the changes in segmentations across modern machine learning/deep learning workflows and use cases.
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Affiliation(s)
- Vishakha Goyal
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA
| | - Nick J Schaub
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA
| | - Ty C Voss
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA
| | - Nathan A Hotaling
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA.
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA.
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36
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Ehsani R, Jonassen I, Akslen LA, Kleftogiannis D. LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies. BIOINFORMATICS ADVANCES 2023; 3:vbad146. [PMID: 37881170 PMCID: PMC10597586 DOI: 10.1093/bioadv/vbad146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/02/2023] [Accepted: 10/10/2023] [Indexed: 10/27/2023]
Abstract
Motivation Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organization of cellular ecosystems remains a challenging problem. Results To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organization and deploys downstream analysis and visualization utilities that can be used for data-driven patient-risk stratification. Our case studies using mass cytometry imaging data from two well-annotated breast cancer cohorts re-confirmed that the spatial organization of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients' survival. Our work introduces an automated and versatile analysis tool for mass cytometry imaging data with many applications in future cancer research projects. Availability and implementation Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR.
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Affiliation(s)
- Rezvan Ehsani
- Department of Informatics, Computational Biology Unit, University of Bergen, Bergen N-5020, Norway
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway
| | - Inge Jonassen
- Department of Informatics, Computational Biology Unit, University of Bergen, Bergen N-5020, Norway
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway
| | - Lars A Akslen
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway
- Department of Pathology, Haukeland University Hospital, Bergen N-5020, Norway
| | - Dimitrios Kleftogiannis
- Department of Informatics, Computational Biology Unit, University of Bergen, Bergen N-5020, Norway
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway
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37
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Bouchard C, Bernatchez R, Lavoie-Cardinal F. Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics. NEUROPHOTONICS 2023; 10:044405. [PMID: 37636490 PMCID: PMC10447257 DOI: 10.1117/1.nph.10.4.044405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/29/2023]
Abstract
Machine learning has revolutionized the way data are processed, allowing information to be extracted in a fraction of the time it would take an expert. In the field of neurophotonics, machine learning approaches are used to automatically detect and classify features of interest in complex images. One of the key challenges in applying machine learning methods to the field of neurophotonics is the scarcity of available data and the complexity associated with labeling them, which can limit the performance of data-driven algorithms. We present an overview of various strategies, such as weakly supervised learning, active learning, and domain adaptation that can be used to address the problem of labeled data scarcity in neurophotonics. We provide a comprehensive overview of the strengths and limitations of each approach and discuss their potential applications to bioimaging datasets. In addition, we highlight how different strategies can be combined to increase model performance on those datasets. The approaches we describe can help to improve the accessibility of machine learning-based analysis with limited number of annotated images for training and can enable researchers to extract more meaningful insights from small datasets.
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Affiliation(s)
- Catherine Bouchard
- CERVO Brain Research Centre, Québec, Québec, Canada
- Université Laval, Institute Intelligence and Data, Québec, Québec, Canada
| | - Renaud Bernatchez
- CERVO Brain Research Centre, Québec, Québec, Canada
- Université Laval, Institute Intelligence and Data, Québec, Québec, Canada
| | - Flavie Lavoie-Cardinal
- CERVO Brain Research Centre, Québec, Québec, Canada
- Université Laval, Institute Intelligence and Data, Québec, Québec, Canada
- Université Laval, Département de psychiatrie et de neurosciences, Québec, Québec, Canada
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38
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Feng X, Yu Z, Fang H, Jiang H, Yang G, Chen L, Zhou X, Hu B, Qin C, Hu G, Xing G, Zhao B, Shi Y, Guo J, Liu F, Han B, Zechmann B, He Y, Liu F. Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy. NATURE PLANTS 2023; 9:1760-1775. [PMID: 37749240 DOI: 10.1038/s41477-023-01527-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 08/25/2023] [Indexed: 09/27/2023]
Abstract
Accurate delineation of plant cell organelles from electron microscope images is essential for understanding subcellular behaviour and function. Here we develop a deep-learning pipeline, called the organelle segmentation network (OrgSegNet), for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei and vacuoles. OrgSegNet was evaluated on a large manually annotated dataset collected from 19 plant species and achieved state-of-the-art segmentation performance. We defined three digital traits (shape complexity, electron density and cross-sectional area) to track the quantitative features of individual organelles in 2D images and released an open-source web tool called Plantorganelle Hunter for quantitatively profiling subcellular morphology. In addition, the automatic segmentation method was successfully applied to a serial-sectioning scanning microscope technique to create a 3D cell model that offers unique views of the morphology and distribution of these organelles. The functionalities of Plantorganelle Hunter can be easily operated, which will increase efficiency and productivity for the plant science community, and enhance understanding of subcellular biology.
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Affiliation(s)
- Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
- The Rural Development Academy & Agricultural Experiment Station, Zhejiang University, Huzhou, China
| | - Zeyu Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- The Rural Development Academy & Agricultural Experiment Station, Zhejiang University, Huzhou, China
| | - Hui Fang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Huzhou Institute of Zhejiang University, Hangzhou, China
| | - Hangjin Jiang
- Center for Data Science, Zhejiang University, Hangzhou, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- The Rural Development Academy & Agricultural Experiment Station, Zhejiang University, Huzhou, China
| | - Liting Chen
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
| | - Xinran Zhou
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
| | - Bing Hu
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
- Biological Experiment Teaching Center, College of Life Sciences, Nanjing Agricultural University, Nanjing, China
| | - Chun Qin
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
- Biological Experiment Teaching Center, College of Life Sciences, Nanjing Agricultural University, Nanjing, China
| | - Gang Hu
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
- Biological Experiment Teaching Center, College of Life Sciences, Nanjing Agricultural University, Nanjing, China
| | - Guipei Xing
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
- Biological Experiment Teaching Center, College of Life Sciences, Nanjing Agricultural University, Nanjing, China
| | - Boxi Zhao
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China
| | - Yongqiang Shi
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jiansheng Guo
- Center of Cryo-Electron Microscopy, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Liu
- School of Mathematics and Statistics, University of Melbourne, Parkville, Australia
| | - Bo Han
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Bernd Zechmann
- Center for Microscopy and Imaging, Baylor University, Waco, TX, USA
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
| | - Feng Liu
- College of Life Sciences, Nanjing Agricultural University, Nanjing, China.
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39
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Jiang J, Zeng Z, Xu J, Wang W, Shi B, Zhu L, Chen Y, Yao W, Wang Y, Zhang H. Long-term, real-time and label-free live cell image processing and analysis based on a combined algorithm of CellPose and watershed segmentation. Heliyon 2023; 9:e20181. [PMID: 37767498 PMCID: PMC10520323 DOI: 10.1016/j.heliyon.2023.e20181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Developing a rapid and quantitative method to accurately evaluate the physiological abilities of living cells is critical for tumor control. Many experiments have been conducted in the field of biology in an attempt to measure the proliferation and movement abilities of cells, but existing methods cannot provide real-time and objective data for label-free cells. The quantitative imaging technique, including an automatic segmentation algorithm for individual label-free cells, has been a breakthrough in this regard. In this study, we develop a combined automatic image processing algorithm of CellPose and watershed segmentation for the long-term and real-time imaging of label-free cells. This method shows strong reliability in cell identification regardless of cell densities, allowing us to obtain accurate information about the number and proliferation ability of the target cells. Additionally, our results also suggest that this method is a reliable way to assess real-time data on drug cytotoxicity, cell morphology, and cell movement ability.
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Affiliation(s)
- Jiang Jiang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin Er Road, Shanghai, 200025, China
| | - Zhikun Zeng
- School of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 200240, China
| | - Jiazhao Xu
- School of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 200240, China
| | - Wenfang Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin Er Road, Shanghai, 200025, China
| | - Bowen Shi
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin Er Road, Shanghai, 200025, China
| | - Lan Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin Er Road, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin Er Road, Shanghai, 200025, China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Shanghai, 200036, China
| | - Yujie Wang
- School of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 200240, China
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, No. 1, Dongsanlu, Erxianqiao, Chengdu, 610059, China
- Department of Physics, College of Mathematics and Physics, Chengdu University of Technology, No. 1, Dongsanlu, Erxianqiao, Chengdu, 610059, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin Er Road, Shanghai, 200025, China
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40
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Meacock OJ, Durham WM. Tracking bacteria at high density with FAST, the Feature-Assisted Segmenter/Tracker. PLoS Comput Biol 2023; 19:e1011524. [PMID: 37812642 PMCID: PMC10586697 DOI: 10.1371/journal.pcbi.1011524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 10/19/2023] [Accepted: 09/17/2023] [Indexed: 10/11/2023] Open
Abstract
Most bacteria live attached to surfaces in densely-packed communities. While new experimental and imaging techniques are beginning to provide a window on the complex processes that play out in these communities, resolving the behaviour of individual cells through time and space remains a major challenge. Although a number of different software solutions have been developed to track microorganisms, these typically require users either to tune a large number of parameters or to groundtruth a large volume of imaging data to train a deep learning model-both manual processes which can be very time consuming for novel experiments. To overcome these limitations, we have developed FAST, the Feature-Assisted Segmenter/Tracker, which uses unsupervised machine learning to optimise tracking while maintaining ease of use. Our approach, rooted in information theory, largely eliminates the need for users to iteratively adjust parameters manually and make qualitative assessments of the resulting cell trajectories. Instead, FAST measures multiple distinguishing 'features' for each cell and then autonomously quantifies the amount of unique information each feature provides. We then use these measurements to determine how data from different features should be combined to minimize tracking errors. Comparing our algorithm with a naïve approach that uses cell position alone revealed that FAST produced 4 to 10 fold fewer tracking errors. The modular design of FAST combines our novel tracking method with tools for segmentation, extensive data visualisation, lineage assignment, and manual track correction. It is also highly extensible, allowing users to extract custom information from images and seamlessly integrate it into downstream analyses. FAST therefore enables high-throughput, data-rich analyses with minimal user input. It has been released for use either in Matlab or as a compiled stand-alone application, and is available at https://bit.ly/3vovDHn, along with extensive tutorials and detailed documentation.
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Affiliation(s)
- Oliver J. Meacock
- Department of Biology, University of Oxford, Oxford, United Kingdom
- Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - William M. Durham
- Department of Biology, University of Oxford, Oxford, United Kingdom
- Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom
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41
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Way GP, Sailem H, Shave S, Kasprowicz R, Carragher NO. Evolution and impact of high content imaging. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2023; 28:292-305. [PMID: 37666456 DOI: 10.1016/j.slasd.2023.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 08/29/2023] [Indexed: 09/06/2023]
Abstract
The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.
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Affiliation(s)
- Gregory P Way
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Heba Sailem
- School of Cancer and Pharmaceutical Sciences, King's College London, UK
| | - Steven Shave
- GlaxoSmithKline Medicines Research Centre, Gunnels Wood Rd, Stevenage SG1 2NY, UK; Edinburgh Cancer Research, Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, UK
| | - Richard Kasprowicz
- GlaxoSmithKline Medicines Research Centre, Gunnels Wood Rd, Stevenage SG1 2NY, UK
| | - Neil O Carragher
- Edinburgh Cancer Research, Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, UK.
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42
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Sockell A, Wong W, Longwell S, Vu T, Karlsson K, Mokhtari D, Schaepe J, Lo YH, Cornelius V, Kuo C, Van Valen D, Curtis C, Fordyce PM. A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids. Cell Syst 2023; 14:764-776.e6. [PMID: 37734323 DOI: 10.1016/j.cels.2023.08.002] [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: 07/29/2022] [Revised: 02/24/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023]
Abstract
Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Alexandra Sockell
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Wing Wong
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Scott Longwell
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Thy Vu
- Department of Biochemistry, UT Austin, Austin, TX 78712, USA
| | - Kasper Karlsson
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Daniel Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - Julia Schaepe
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Yuan-Hung Lo
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Vincent Cornelius
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Calvin Kuo
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Christina Curtis
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA.
| | - Polly M Fordyce
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA.
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43
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Mikheyeva IV, Sun J, Huang KC, Silhavy TJ. Mechanism of outer membrane destabilization by global reduction of protein content. Nat Commun 2023; 14:5715. [PMID: 37714857 PMCID: PMC10504340 DOI: 10.1038/s41467-023-40396-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/26/2023] [Indexed: 09/17/2023] Open
Abstract
The outer membrane (OM) of Gram-negative bacteria such as Escherichia coli is an asymmetric bilayer with the glycolipid lipopolysaccharide (LPS) in the outer leaflet and glycerophospholipids in the inner. Nearly all integral OM proteins (OMPs) have a characteristic β-barrel fold and are assembled in the OM by the BAM complex, which contains one essential β-barrel protein (BamA), one essential lipoprotein (BamD), and three non-essential lipoproteins (BamBCE). A gain-of-function mutation in bamA enables survival in the absence of BamD, showing that the essential function of this protein is regulatory. Here, we demonstrate that the global reduction in OMPs caused by BamD loss weakens the OM, altering cell shape and causing OM rupture in spent medium. To fill the void created by OMP loss, phospholipids (PLs) flip into the outer leaflet. Under these conditions, mechanisms that remove PLs from the outer leaflet create tension between the OM leaflets, which contributes to membrane rupture. Rupture is prevented by suppressor mutations that release the tension by halting PL removal from the outer leaflet. However, these suppressors do not restore OM stiffness or normal cell shape, revealing a possible connection between OM stiffness and cell shape.
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Affiliation(s)
- Irina V Mikheyeva
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08540, USA
| | - Jiawei Sun
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Kerwyn Casey Huang
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
| | - Thomas J Silhavy
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08540, USA.
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44
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Knapp BD, Willis L, Gonzalez C, Vashistha H, Touma JJ, Tikhonov M, Ram J, Salman H, Elias JE, Huang KC. Metabolomic rearrangement controls the intrinsic microbial response to temperature changes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.22.550177. [PMID: 37546722 PMCID: PMC10401945 DOI: 10.1101/2023.07.22.550177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Temperature is one of the key determinants of microbial behavior and survival, whose impact is typically studied under heat- or cold-shock conditions that elicit specific regulation to combat lethal stress. At intermediate temperatures, cellular growth rate varies according to the Arrhenius law of thermodynamics without stress responses, a behavior whose origins have not yet been elucidated. Using single-cell microscopy during temperature perturbations, we show that bacteria exhibit a highly conserved, gradual response to temperature upshifts with a time scale of ~1.5 doublings at the higher temperature, regardless of initial/final temperature or nutrient source. We find that this behavior is coupled to a temperature memory, which we rule out as being neither transcriptional, translational, nor membrane dependent. Instead, we demonstrate that an autocatalytic enzyme network incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, which encodes a temperature memory and successfully predicts alterations in the upshift response observed under simple-sugar, low-nutrient conditions, and in fungi. This model also provides a mechanistic framework for both Arrhenius-dependent growth and the classical Monod Equation through temperature-dependent metabolite flux.
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Affiliation(s)
| | - Lisa Willis
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Carlos Gonzalez
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Harsh Vashistha
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Joanna Jammal Touma
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Mikhail Tikhonov
- Department of Physics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Jeffrey Ram
- Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Hanna Salman
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Josh E. Elias
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Kerwyn Casey Huang
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
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45
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Shiver AL, Sun J, Culver R, Violette A, Wynter C, Nieckarz M, Mattiello SP, Sekhon PK, Friess L, Carlson HK, Wong D, Higginbottom S, Weglarz M, Wang W, Knapp BD, Guiberson E, Sanchez J, Huang PH, Garcia PA, Buie CR, Good B, DeFelice B, Cava F, Scaria J, Sonnenburg J, Sinderen DV, Deutschbauer AM, Huang KC. A mutant fitness compendium in Bifidobacteria reveals molecular determinants of colonization and host-microbe interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.29.555234. [PMID: 37693407 PMCID: PMC10491234 DOI: 10.1101/2023.08.29.555234] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Bifidobacteria commonly represent a dominant constituent of human gut microbiomes during infancy, influencing nutrition, immune development, and resistance to infection. Despite interest as a probiotic therapy, predicting the nutritional requirements and health-promoting effects of Bifidobacteria is challenging due to major knowledge gaps. To overcome these deficiencies, we used large-scale genetics to create a compendium of mutant fitness in Bifidobacterium breve (Bb). We generated a high density, randomly barcoded transposon insertion pool in Bb, and used this pool to determine Bb fitness requirements during colonization of germ-free mice and chickens with multiple diets and in response to hundreds of in vitro perturbations. To enable mechanistic investigation, we constructed an ordered collection of insertion strains covering 1462 genes. We leveraged these tools to improve models of metabolic pathways, reveal unexpected host- and diet-specific requirements for colonization, and connect the production of immunomodulatory molecules to growth benefits. These resources will greatly reduce the barrier to future investigations of this important beneficial microbe.
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Affiliation(s)
- Anthony L. Shiver
- Department of Bioengineering, Stanford University, Stanford CA 94305, USA
| | - Jiawei Sun
- Department of Bioengineering, Stanford University, Stanford CA 94305, USA
| | - Rebecca Culver
- Department of Genetics, Stanford University, Stanford CA 94305, USA
| | - Arvie Violette
- Department of Bioengineering, Stanford University, Stanford CA 94305, USA
| | - Charles Wynter
- Department of Bioengineering, Stanford University, Stanford CA 94305, USA
| | - Marta Nieckarz
- Department of Molecular Biology and Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå Centre for Microbial Research (UCMR), Umeå University, Umeå, SE-90187, Sweden
| | - Samara Paula Mattiello
- Department of Veterinary and Biomedical Sciences, South Dakota State University, Brookings, SD, 57007, USA
- College of Mathematics and Science, The University of Tennessee Southern, Pulaski TN 38478, USA
| | - Prabhjot Kaur Sekhon
- Department of Veterinary and Biomedical Sciences, South Dakota State University, Brookings, SD, 57007, USA
- Department of Veterinary Pathobiology, Oklahoma State University, Stillwater, OK, 74074, USA
| | - Lisa Friess
- School of Microbiology, University College Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
| | - Hans K. Carlson
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Wong
- Department of Applied Physics, Stanford University, Stanford CA 94305, USA
| | - Steven Higginbottom
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Meredith Weglarz
- Stanford Shared FACS Facility, Center for Molecular and Genetic Medicine, Stanford University, Stanford, California, USA
| | - Weigao Wang
- Department of Chemical Engineering, Stanford University, Stanford CA 94305, USA
| | | | - Emma Guiberson
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Po-Hsun Huang
- Department of Mechanical Engineering, Laboratory for Energy and Microsystems Innovation, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Paulo A. Garcia
- Department of Mechanical Engineering, Laboratory for Energy and Microsystems Innovation, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Cullen R. Buie
- Department of Mechanical Engineering, Laboratory for Energy and Microsystems Innovation, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Benjamin Good
- Department of Applied Physics, Stanford University, Stanford CA 94305, USA
| | | | - Felipe Cava
- Department of Molecular Biology and Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå Centre for Microbial Research (UCMR), Umeå University, Umeå, SE-90187, Sweden
| | - Joy Scaria
- Department of Veterinary and Biomedical Sciences, South Dakota State University, Brookings, SD, 57007, USA
- Department of Veterinary Pathobiology, Oklahoma State University, Stillwater, OK, 74074, USA
| | - Justin Sonnenburg
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Douwe Van Sinderen
- School of Microbiology, University College Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
| | - Adam M. Deutschbauer
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Kerwyn Casey Huang
- Department of Bioengineering, Stanford University, Stanford CA 94305, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA 94158
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46
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Che VL, Zimmermann J, Zhou Y, Lu XL, van Rienen U. Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images. Front Bioeng Biotechnol 2023; 11:1225495. [PMID: 37711443 PMCID: PMC10497969 DOI: 10.3389/fbioe.2023.1225495] [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: 05/19/2023] [Accepted: 08/07/2023] [Indexed: 09/16/2023] Open
Abstract
Electric fields find use in tissue engineering but also in sensor applications besides the broad classical application range. Accurate numerical models of electrical stimulation devices can pave the way for effective therapies in cartilage regeneration. To this end, the dielectric properties of the electrically stimulated tissue have to be known. However, knowledge of the dielectric properties is scarce. Electric field-based methods such as impedance spectroscopy enable determining the dielectric properties of tissue samples. To develop a detailed understanding of the interaction of the employed electric fields and the tissue, fine-grained numerical models based on tissue-specific 3D geometries are considered. A crucial ingredient in this approach is the automated generation of numerical models from biomedical images. In this work, we explore classical and artificial intelligence methods for volumetric image segmentation to generate model geometries. We find that deep learning, in particular the StarDist algorithm, permits fast and automatic model geometry and discretisation generation once a sufficient amount of training data is available. Our results suggest that already a small number of 3D images (23 images) is sufficient to achieve 80% accuracy on the test data. The proposed method enables the creation of high-quality meshes without the need for computer-aided design geometry post-processing. Particularly, the computational time for the geometrical model creation was reduced by half. Uncertainty quantification as well as a direct comparison between the deep learning and the classical approach reveal that the numerical results mainly depend on the cell volume. This result motivates further research into impedance sensors for tissue characterisation. The presented approach can significantly improve the accuracy and computational speed of image-based models of electrical stimulation for tissue engineering applications.
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Affiliation(s)
- Vien Lam Che
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
| | - Julius Zimmermann
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
| | - Yilu Zhou
- Department of Mechanical Engineering, University of Delaware, Delaware, DE, United States
| | - X. Lucas Lu
- Department of Mechanical Engineering, University of Delaware, Delaware, DE, United States
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
- Department Life, Light and Matter, University of Rostock, Rostock, Germany
- Department of Ageing of Individuals and Society, Interdisciplinary Faculty, University of Rostock, Rostock, Germany
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47
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Basak US, Sattari S, Hossain MM, Horikawa K, Toda M, Komatsuzaki T. Comparison of particle image velocimetry and the underlying agents dynamics in collectively moving self propelled particles. Sci Rep 2023; 13:12566. [PMID: 37532878 PMCID: PMC10397335 DOI: 10.1038/s41598-023-39635-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/28/2023] [Indexed: 08/04/2023] Open
Abstract
Collective migration of cells is a fundamental behavior in biology. For the quantitative understanding of collective cell migration, live-cell imaging techniques have been used using e.g., phase contrast or fluorescence images. Particle tracking velocimetry (PTV) is a common recipe to quantify cell motility with those image data. However, the precise tracking of cells is not always feasible. Particle image velocimetry (PIV) is an alternative to PTV, corresponding to Eulerian picture of fluid dynamics, which derives the average velocity vector of an aggregate of cells. However, the accuracy of PIV in capturing the underlying cell motility and what values of the parameters should be chosen is not necessarily well characterized, especially for cells that do not adhere to a viscous flow. Here, we investigate the accuracy of PIV by generating images of simulated cells by the Vicsek model using trajectory data of agents at different noise levels. It was found, using an alignment score, that the direction of the PIV vectors coincides with the direction of nearby agents with appropriate choices of PIV parameters. PIV is found to accurately measure the underlying motion of individual agents for a wide range of noise level, and its condition is addressed.
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Affiliation(s)
- Udoy S Basak
- Pabna University of Science and Technology, Pabna, 6600, Bangladesh
| | - Sulimon Sattari
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Kita 20, Nishi 10, Kita-ku, Sapporo, 001-0020, Japan.
| | - Md Motaleb Hossain
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Kita 20, Nishi 10, Kita-ku, Sapporo, 001-0020, Japan
- University of Dhaka, Dhaka, 1000, Bangladesh
| | - Kazuki Horikawa
- Department of Optical Imaging, Advanced Research Promotion Center, Tokushima University, Kuramoto-cho 3-18-15, Tokushima, Tokushima, 770-8503, Japan
| | - Mikito Toda
- Faculty Division of Natural Sciences, Research Group of Physics, Nara Women's University, Kita-Uoya-Nishimachi, Nara, 630-8506, Japan
- Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan
- Research Institute for Electronic Science, Hokkaido University, Kita 20 Nishi 10, Kita-Ku, Sapporo, 001-0020, Japan
| | - Tamiki Komatsuzaki
- Pabna University of Science and Technology, Pabna, 6600, Bangladesh.
- Graduate School of Life Science, Transdisciplinary Life Science Course, Hokkaido University, Kita 12, Nishi 6, Kita-ku, Sapporo, 060-0812, Japan.
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido, 001-0021, Japan.
- Graduate School of Chemical Sciences and Engineering Materials Chemistry and Engineering Course, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo, 060-0812, Japan.
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Yamadaoka, Suita, Osaka, 565-0871, Japan.
- The Institute of Scientific and Industrial Research, Osaka University, Mihogaoka, 8-1, Osaka, Ibaraki, 567-0047, Japan.
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48
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Mac Gabhann F, Pitzer VE, Papin JA. The blossoming of methods and software in computational biology. PLoS Comput Biol 2023; 19:e1011390. [PMID: 37590323 PMCID: PMC10434930 DOI: 10.1371/journal.pcbi.1011390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023] Open
Affiliation(s)
- Feilim Mac Gabhann
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Virginia E. Pitzer
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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Zidane M, Makky A, Bruhns M, Rochwarger A, Babaei S, Claassen M, Schürch CM. A review on deep learning applications in highly multiplexed tissue imaging data analysis. FRONTIERS IN BIOINFORMATICS 2023; 3:1159381. [PMID: 37564726 PMCID: PMC10410935 DOI: 10.3389/fbinf.2023.1159381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 07/12/2023] [Indexed: 08/12/2023] Open
Abstract
Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.
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Affiliation(s)
- Mohammed Zidane
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Ahmad Makky
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Matthias Bruhns
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sepideh Babaei
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
| | - Manfred Claassen
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Christian M. Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
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50
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Alonso A, Kirkegaard JB. Fast detection of slender bodies in high density microscopy data. Commun Biol 2023; 6:754. [PMID: 37468539 PMCID: PMC10356847 DOI: 10.1038/s42003-023-05098-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/05/2023] [Indexed: 07/21/2023] Open
Abstract
Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as swimming nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping slender bodies. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of swimming Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.
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Affiliation(s)
- Albert Alonso
- Niels Bohr Institute & Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Julius B Kirkegaard
- Niels Bohr Institute & Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
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