1
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Ghanegolmohammadi F, Eslami M, Ohya Y. Systematic data analysis pipeline for quantitative morphological cell phenotyping. Comput Struct Biotechnol J 2024; 23:2949-2962. [PMID: 39104709 PMCID: PMC11298594 DOI: 10.1016/j.csbj.2024.07.012] [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: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepen’s Eye Research Institute of Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, USA
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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2
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Subedi S, Sumida TS, Park YP. A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection. Life Sci Alliance 2024; 7:e202402713. [PMID: 39107066 PMCID: PMC11303850 DOI: 10.26508/lsa.202402713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 08/09/2024] Open
Abstract
Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.
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Affiliation(s)
- Sishir Subedi
- https://ror.org/03rmrcq20Bioinformatics Graduate Program, University of British Columbia, Vancouver, Canada
- BC Cancer Research, Vancouver, Canada
| | - Tomokazu S Sumida
- Neurology, Program for Neuroinflammation, Yale School of Medicine, New Haven, CT, USA
| | - Yongjin P Park
- BC Cancer Research, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
- Department of Statistics, University of British Columbia, Vancouver, Canada
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3
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Trapnell C. Revealing gene function with statistical inference at single-cell resolution. Nat Rev Genet 2024; 25:623-638. [PMID: 38951690 DOI: 10.1038/s41576-024-00750-w] [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: 05/21/2024] [Indexed: 07/03/2024]
Abstract
Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.
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Affiliation(s)
- Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
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4
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Luo S, Germain PL, Robinson MD, von Meyenn F. Benchmarking computational methods for single-cell chromatin data analysis. Genome Biol 2024; 25:225. [PMID: 39152456 PMCID: PMC11328424 DOI: 10.1186/s13059-024-03356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 07/29/2024] [Indexed: 08/19/2024] Open
Abstract
BACKGROUND Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices. RESULTS We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection. CONCLUSIONS Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.
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Affiliation(s)
- Siyuan Luo
- Laboratory of Nutrition and Metabolic Epigenetics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Pierre-Luc Germain
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
- Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
| | - Ferdinand von Meyenn
- Laboratory of Nutrition and Metabolic Epigenetics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
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5
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Verdin A, Malherbe C, Eppe G. Designing SERS nanotags for profiling overexpressed surface markers on single cancer cells: A review. Talanta 2024; 276:126225. [PMID: 38749157 DOI: 10.1016/j.talanta.2024.126225] [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: 03/13/2024] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 06/14/2024]
Abstract
This review focuses on the chemical design and the use of Surface-Enhanced Raman Scattering (SERS)-active nanotags for measuring surface markers that can be overexpressed at the surface of single cancer cells. Indeed, providing analytical tools with true single-cell measurements capabilities is capital, especially since cancer research is increasingly leaning toward single-cell analysis, either to guide treatment decisions or to understand complex tumor behaviour including the single-cell heterogeneity and the appearance of treatment resistance. Over the past two decades, SERS nanotags have triggered significant interest in the scientific community owing their advantages over fluorescent tags, mainly because SERS nanotags resist photobleaching and exhibit sharper signal bands, which reduces possible spectral overlap and enables the discrimination between the SERS signals and the autofluorescence background from the sample itself. The extensive efforts invested in harnessing SERS nanotags for biomedical purposes, particularly in cancer research, highlight their potential as the next generation of optical labels for single-cell studies. The review unfolds in two main parts. The first part focuses on the structure of SERS nanotags, detailing their chemical composition and the role of each building block of the tags. The second part explores applications in measuring overexpressed surface markers on single-cells. The latter encompasses studies using single nanotags, multiplexed measurements, quantitative information extraction, monitoring treatment responses, and integrating phenotype measurements with SERS nanotags on single cells isolated from complex biological matrices. This comprehensive review anticipates SERS nanotags to persist as a pivotal technology in advancing single-cell analytical methods, particularly in the context of cancer research and personalized medicine.
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Affiliation(s)
- Alexandre Verdin
- Mass Spectrometry Laboratory, MolSys Research Unit, University of Liège, Belgium.
| | - Cedric Malherbe
- Mass Spectrometry Laboratory, MolSys Research Unit, University of Liège, Belgium
| | - Gauthier Eppe
- Mass Spectrometry Laboratory, MolSys Research Unit, University of Liège, Belgium
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6
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Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline SJC, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. BIOINFORMATICS ADVANCES 2024; 4:vbae099. [PMID: 39143982 PMCID: PMC11321866 DOI: 10.1093/bioadv/vbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.
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Affiliation(s)
- Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Aydin Wells
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Deisy Morselli Gysi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Statistics, Federal University of Paraná, Curitiba, Paraná 81530-015, Brazil
- Department of Physics, Northeastern University, Boston, MA 02115, United States
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Wisconsin Institute for Discovery, Madison, WI 53715, United States
| | - Anaïs Baudot
- Aix Marseille Université, INSERM, MMG, Marseille, France
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- Department of Mathematics, University of North Texas, Denton, TX 76203, United States
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Morgridge Institute for Research, Madison, WI 53715, United States
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Seattle, WA 98109, United States
| | - Pengfei Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pietro H Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, 88100, Italy
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, MD 20742, United States
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Ziynet Nesibe Kesimoglu
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, United States
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, WC1E 6BT, England
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Hanghang Tong
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Xinan Holly Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, United States
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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7
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Rodrigues-Amorim D, Bozzelli PL, Kim T, Liu L, Gibson O, Yang CY, Murdock MH, Galiana-Melendez F, Schatz B, Davison A, Islam MR, Shin Park D, Raju RM, Abdurrob F, Nelson AJ, Min Ren J, Yang V, Stokes MP, Tsai LH. Multisensory gamma stimulation mitigates the effects of demyelination induced by cuprizone in male mice. Nat Commun 2024; 15:6744. [PMID: 39112447 PMCID: PMC11306744 DOI: 10.1038/s41467-024-51003-7] [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: 09/02/2023] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
Demyelination is a common pathological feature in a wide range of diseases, characterized by the loss of myelin sheath and myelin-supporting oligodendrocytes. These losses lead to impaired axonal function, increased vulnerability of axons to damage, and result in significant brain atrophy and neuro-axonal degeneration. Multiple pathomolecular processes contribute to neuroinflammation, oligodendrocyte cell death, and progressive neuronal dysfunction. In this study, we use the cuprizone mouse model of demyelination to investigate long-term non-invasive gamma entrainment using sensory stimulation as a potential therapeutic intervention for promoting myelination and reducing neuroinflammation in male mice. Here, we show that multisensory gamma stimulation mitigates demyelination, promotes oligodendrogenesis, preserves functional integrity and synaptic plasticity, attenuates oligodendrocyte ferroptosis-induced cell death, and reduces brain inflammation. Thus, the protective effects of multisensory gamma stimulation on myelin and anti-neuroinflammatory properties support its potential as a therapeutic approach for demyelinating disorders.
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Grants
- R01 AG069232 NIA NIH HHS
- R01 AT011460 NCCIH NIH HHS
- R01 NS122742 NINDS NIH HHS
- R56 AG069232 NIA NIH HHS
- We would like to acknowledge the following individuals and organizations for their support: Fundacion Bancaria la Caixa, The JPB Foundation, Carol and Gene Ludwig Family Foundation, Lester A. Gimpelson, Eduardo Eurnekian, The Dolby Family, Kathy and Miguel Octavio, the Marc Haas Foundation, Ben Lenail and Laurie Yoler, and NIH RO1 grants AG069232, AT011460 and R01NS122742 to L.-H.T.
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Affiliation(s)
- Daniela Rodrigues-Amorim
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P Lorenzo Bozzelli
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - TaeHyun Kim
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Liwang Liu
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Cheng-Yi Yang
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mitchell H Murdock
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fabiola Galiana-Melendez
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brooke Schatz
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alexis Davison
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Md Rezaul Islam
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dong Shin Park
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ravikiran M Raju
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Newborn Medicine, Harvard Medical School, Boston Children's Hospital, Boston, MA, USA
| | - Fatema Abdurrob
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Jian Min Ren
- Cell Signaling Technology, 3 Trask Lane, Danvers, MA, USA
| | - Vicky Yang
- Cell Signaling Technology, 3 Trask Lane, Danvers, MA, USA
| | | | - Li-Huei Tsai
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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8
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Fischer F, Fischer DS, Mukhin R, Isaev A, Biederstedt E, Villani AC, Theis FJ. scTab: Scaling cross-tissue single-cell annotation models. Nat Commun 2024; 15:6611. [PMID: 39098889 PMCID: PMC11298532 DOI: 10.1038/s41467-024-51059-5] [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: 11/26/2023] [Accepted: 07/25/2024] [Indexed: 08/06/2024] Open
Abstract
Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.
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Affiliation(s)
- Felix Fischer
- Department of Computational Health, Institute of Computational Biology, Helmholtz, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - David S Fischer
- Department of Computational Health, Institute of Computational Biology, Helmholtz, Munich, Germany
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | | | | | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Fabian J Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz, Munich, Germany.
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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9
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Wang S, Li H, Liu Y, Pang S, Qiao S, Su J, Wang S, Zhang Y. Connectivity Network Feature Sharing in Single-Cell RNA Sequencing Data Identifies Rare Cells. J Chem Inf Model 2024. [PMID: 39096508 DOI: 10.1021/acs.jcim.4c00796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2024]
Abstract
Single-cell RNA sequencing is a valuable technique for identifying diverse cell subtypes. A key challenge in this process is that the detection of rare cells is often missed by conventional methods due to low abundance and subtle features of these cells. To overcome this, we developed SCLCNF (Local Connectivity Network Feature Sharing in Single-Cell RNA sequencing), a novel approach that identifies rare cells by analyzing features uniquely expressed in these cells. SCLCNF creates a cellular connectivity network, considering how each cell relates to its neighbors. This network helps to pinpoint coexpression patterns unique to rare cells, utilizing a rarity score to confirm their presence. Our method performs better in detecting rare cells than existing techniques, offering enhanced robustness. It has proven to be effective in human gastrula data sets for accurately pinpointing rare cells, and in sepsis data sets where it uncovers previously unidentified rare cell populations.
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Affiliation(s)
- Shudong Wang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Hengxiao Li
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Yahui Liu
- College of Science, China University of Petroleum (East China), Qingdao 266580, China
| | - Shanchen Pang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Sibo Qiao
- The College of Software, Tiangong University, Tianjin 300387, China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, China
| | - Shaoqiang Wang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China
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10
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Korb A, Tajbakhsh S, Comai GE. Functional specialisation and coordination of myonuclei. Biol Rev Camb Philos Soc 2024; 99:1164-1195. [PMID: 38477382 DOI: 10.1111/brv.13063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 01/30/2024] [Accepted: 02/02/2024] [Indexed: 03/14/2024]
Abstract
Myofibres serve as the functional unit for locomotion, with the sarcomere as fundamental subunit. Running the entire length of this structure are hundreds of myonuclei, located at the periphery of the myofibre, juxtaposed to the plasma membrane. Myonuclear specialisation and clustering at the centre and ends of the fibre are known to be essential for muscle contraction, yet the molecular basis of this regionalisation has remained unclear. While the 'myonuclear domain hypothesis' helped explain how myonuclei can independently govern large cytoplasmic territories, novel technologies have provided granularity on the diverse transcriptional programs running simultaneously within the syncytia and added a new perspective on how myonuclei communicate. Building upon this, we explore the critical cellular and molecular sources of transcriptional and functional heterogeneity within myofibres, discussing the impact of intrinsic and extrinsic factors on myonuclear programs. This knowledge provides new insights for understanding muscle development, repair, and disease, but also opens avenues for the development of novel and precise therapeutic approaches.
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Affiliation(s)
- Amaury Korb
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
| | - Shahragim Tajbakhsh
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
| | - Glenda E Comai
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
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11
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401815. [PMID: 38887194 PMCID: PMC11336957 DOI: 10.1002/advs.202401815] [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: 02/20/2024] [Revised: 04/22/2024] [Indexed: 06/20/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Affiliation(s)
- Jiajia Liu
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Jian Ma
- Department of Electronic Information and Computer EngineeringThe Engineering & Technical College of Chengdu University of TechnologyLeshanSichuan614000China
| | - Jianguo Wen
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- School of DentistryThe University of Texas Health Science Center at HoustonHoustonTX77030USA
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12
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Szałata A, Hrovatin K, Becker S, Tejada-Lapuerta A, Cui H, Wang B, Theis FJ. Transformers in single-cell omics: a review and new perspectives. Nat Methods 2024; 21:1430-1443. [PMID: 39122952 DOI: 10.1038/s41592-024-02353-z] [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: 12/19/2023] [Accepted: 06/07/2024] [Indexed: 08/12/2024]
Abstract
Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.
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Affiliation(s)
- Artur Szałata
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Karin Hrovatin
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Sören Becker
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Munich Center of Machine Learning, Munich, Germany
| | - Alejandro Tejada-Lapuerta
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Haotian Cui
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
| | - Bo Wang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- AI Hub, University Health Network, Toronto, Ontario, Canada
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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13
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Ferreira Neres D, Wright RC. Pleiotropy, a feature or a bug? Toward co-ordinating plant growth, development, and environmental responses through engineering plant hormone signaling. Curr Opin Biotechnol 2024; 88:103151. [PMID: 38823314 PMCID: PMC11316663 DOI: 10.1016/j.copbio.2024.103151] [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: 03/01/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 06/03/2024]
Abstract
The advent of gene editing technologies such as CRISPR has simplified co-ordinating trait development. However, identifying candidate genes remains a challenge due to complex gene networks and pathways. These networks exhibit pleiotropy, complicating the determination of specific gene and pathway functions. In this review, we explore how systems biology and single-cell sequencing technologies can aid in identifying candidate genes for co-ordinating specifics of plant growth and development within specific temporal and tissue contexts. Exploring sequence-function space of these candidate genes and pathway modules with synthetic biology allows us to test hypotheses and define genotype-phenotype relationships through reductionist approaches. Collectively, these techniques hold the potential to advance breeding and genetic engineering strategies while also addressing genetic diversity issues critical for adaptation and trait development.
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Affiliation(s)
- Deisiany Ferreira Neres
- Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States; Translational Plant Science Center, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States
| | - R Clay Wright
- Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States; Translational Plant Science Center, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States.
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14
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Filippi J, Casti P, Antonelli G, Murdocca M, Mencattini A, Corsi F, D'Orazio M, Pecora A, De Luca M, Curci G, Ghibelli L, Sangiuolo F, Neale SL, Martinelli E. Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In-Flow Identification of Single Cells. SMALL METHODS 2024; 8:e2300923. [PMID: 38693090 DOI: 10.1002/smtd.202300923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 04/04/2024] [Indexed: 05/03/2024]
Abstract
A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi-frequency cell trajectories. Like in a "ping-pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so-derived cell motions are characterized via time-lapse microscopy, cell tracking, and state-of-the-art machine learning algorithms, like the wavelet scattering transform (WST). As a cell-electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug-induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine-like oxidized low-density lipoprotein receptor-1 (LOX-1) transcript levels in human colorectal adenocarcinoma (DLD-1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP-based assays addressing cancer heterogeneity for precision medicine and pharmacological research.
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Affiliation(s)
- Joanna Filippi
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Paola Casti
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Gianni Antonelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Michela Murdocca
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, Rome, 00133, Italy
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Francesca Corsi
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
- Department of Chemical Science and Technologies, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
| | - Michele D'Orazio
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Alessandro Pecora
- Italian Nation Research Council (CNR), Via del Fosso del Cavaliere 100, Rome, 00133, Italy
| | - Massimiliano De Luca
- Italian Nation Research Council (CNR), Via del Fosso del Cavaliere 100, Rome, 00133, Italy
| | - Giorgia Curci
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Lina Ghibelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
| | - Federica Sangiuolo
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, Rome, 00133, Italy
| | - Steven L Neale
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
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15
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Atia GA, Rashed F, Taher ES, Cho SG, Dayem AA, Soliman MM, Shalaby HK, Mohammed NA, Taymour N, El-Sherbiny M, Ebrahim E, Ramadan MM, Abdelkader A, Abdo M, Aldarmahi AA, Atwa AM, Bafail DA, Abdeen A. Challenges of therapeutic applications and regenerative capacities of urine based stem cells in oral, and maxillofacial reconstruction. Biomed Pharmacother 2024; 177:117005. [PMID: 38945084 DOI: 10.1016/j.biopha.2024.117005] [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: 03/31/2024] [Revised: 06/09/2024] [Accepted: 06/17/2024] [Indexed: 07/02/2024] Open
Abstract
Urine-derived stem cells (USCs) have gained the attention of researchers in the biomedical field in the past few years . Regarding the several varieties of cells that have been used for this purpose, USCs have demonstrated mesenchymal stem cell-like properties, such as differentiation and immunomodulation. Furthermore, they could be differentiated into several lineages. This is very interesting for regenerative techniques based on cell therapy. This review will embark on describing their separation, and profiling. We will specifically describe the USCs characteristics, in addition to their differentiation potential. Then, we will introduce and explore the primary uses of USCs. These involve thier utilization as a platform to produce stem cells, however, we shall concentrate on the utilization of USCs for therapeutic, and regenerative orofacial applications, providing an in-depth evaluation of this purpose. The final portion will address the limitations and challenges of their implementation in regenerative dentistry.
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Affiliation(s)
- Gamal A Atia
- Department of Oral Medicine, Periodontology, and Diagnosis, Faculty of Dentistry, Suez Canal University, Ismailia 41522, Egypt.
| | - Fatema Rashed
- Department of Basic Medical and Dental Sciences, Faculty of Dentistry, Zarqa University, Zarqa 13110, Jordan
| | - Ehab S Taher
- Department of Basic Medical and Dental Sciences, Faculty of Dentistry, Zarqa University, Zarqa 13110, Jordan
| | - Ssang-Goo Cho
- Department of Stem Cell and Regenerative Biotechnology and Institute of Advanced Regenerative Science, Konkuk University, Seoul 05029, South Korea.
| | - Ahmed Abdal Dayem
- Department of Stem Cell and Regenerative Biotechnology and Institute of Advanced Regenerative Science, Konkuk University, Seoul 05029, South Korea
| | - Magdalen M Soliman
- Department of Oral Medicine, Periodontology, and Diagnosis, Faculty of Dentistry, Badr University, Egypt
| | - Hany K Shalaby
- Department of Oral Medicine, Periodontology and Oral Diagnosis, Faculty of Dentistry, Suez University, Suez 43512, Egypt
| | - Nourelhuda A Mohammed
- Physiology and Biochemistry Department, Faculty of Medicine, Mutah University, Mutah, Al-Karak 61710, Jordan
| | - Noha Taymour
- Department of Substitutive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohamed El-Sherbiny
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, 71666, Riyadh 11597, Saudi Arabia; Department of Anatomy, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
| | - Elturabi Ebrahim
- Department of Medical Surgical Nursing, Nursing College, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Mahmoud M Ramadan
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Afaf Abdelkader
- Department of Forensic Medicine and Clinical Toxicology, Faculty of Medicine, Benha University, Benha 13518, Egypt
| | - Mohamed Abdo
- Department of Animal Histology and Anatomy, School of Veterinary Medicine, Badr University in Cairo (BUC), Badr City, Egypt; Department of Anatomy and Embryology, Faculty Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Ahmed A Aldarmahi
- Department of Basic Science, College of Science and Health Professions, King Saud bin Abdulaziz University for Health Sciences, Jeddah 21582, Saudi Arabia; National Guard, Health Affairs, King Abdullah International Medical Research Centre, Jeddah 21582, Saudi Arabia
| | - Ahmed M Atwa
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Egyptian Russian University, Cairo 11829, Egypt
| | - Duaa A Bafail
- Department of Clinical Pharmacology, Faculty of Medicine, King Abdulaziz University, Jeddah 11829, Saudi Arabia
| | - Ahmed Abdeen
- Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, Benha University, Toukh 13736, Egypt.
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16
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Yang X, Mann KK, Wu H, Ding J. scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration. Genome Biol 2024; 25:198. [PMID: 39075536 PMCID: PMC11285326 DOI: 10.1186/s13059-024-03338-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: 12/15/2023] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.
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Affiliation(s)
- Xiuhui Yang
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Koren K Mann
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Hao Wu
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China.
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada.
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada.
- Mila-Quebec AI Institute, Montreal, QC, H2S 3H1, Canada.
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17
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Arici MK, Tuncbag N. Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction. Brief Bioinform 2024; 25:bbae399. [PMID: 39163205 PMCID: PMC11334722 DOI: 10.1093/bib/bbae399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/26/2024] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.
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Affiliation(s)
- Muslum Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara 06800, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul 34450, Turkey
- School of Medicine, Koc University, Istanbul 34450, Turkey
- Koc University Research Center for Translational Medicine (KUTTAM), Koc University, Istanbul 34450, Turkey
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18
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Venkat A, Carlino MJ, Lawton BR, Prasad ML, Amodio M, Gibson CE, Zeiss CJ, Youlten SE, Krishnaswamy S, Krause DS. Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue. Genome Res 2024; 34:837-850. [PMID: 38977309 PMCID: PMC11293540 DOI: 10.1101/gr.278215.123] [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: 07/01/2023] [Accepted: 06/03/2024] [Indexed: 07/10/2024]
Abstract
Studies on human parathyroids are generally limited to hyperfunctioning glands owing to the difficulty in obtaining normal human tissue. We therefore obtained non-human primate (NHP) parathyroids to provide a suitable alternative for sequencing that would bear a close semblance to human organs. Single-cell RNA expression analysis of parathyroids from four healthy adult M. mulatta reveals a continuous trajectory of epithelial cell states. Pseudotime analysis based on transcriptomic signatures suggests a progression from GCM2 hi progenitors to mature parathyroid hormone (PTH)-expressing epithelial cells with increasing core mitochondrial transcript abundance along pseudotime. We sequenced, as a comparator, four histologically characterized hyperfunctioning human parathyroids with varying oxyphil and chief cell abundance and leveraged advanced computational techniques to highlight similarities and differences from non-human primate parathyroid expression dynamics. Predicted cell-cell communication analysis reveals abundant endothelial cell interactions in the parathyroid cell microenvironment in both human and NHP parathyroid glands. We show abundant RARRES2 transcripts in both human adenoma and normal primate parathyroid cells and use coimmunostaining to reveal high levels of RARRES2 protein (also known as chemerin) in PTH-expressing cells, which could indicate that RARRES2 plays an unrecognized role in parathyroid endocrine function. The data obtained are the first single-cell RNA transcriptome to characterize nondiseased parathyroid cell signatures and to show a transcriptomic progression of cell states within normal parathyroid glands, which can be used to better understand parathyroid cell biology.
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Affiliation(s)
- Aarthi Venkat
- Computational Biology and Bioinformatics Program, Yale University, New Haven, Connecticut 06511, USA
| | - Maximillian J Carlino
- Yale Stem Cell Center, Yale School of Medicine, New Haven, Connecticut 06520, USA
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut 06510, USA
| | - Betty R Lawton
- Yale Stem Cell Center, Yale School of Medicine, New Haven, Connecticut 06520, USA
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut 06510, USA
| | - Manju L Prasad
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut 06520-8023, USA
| | - Matthew Amodio
- Department of Computer Science, Yale University, New Haven, Connecticut 06511, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Courtney E Gibson
- Department of Surgery, Yale School of Medicine, New Haven, Connecticut 06520, USA
| | - Caroline J Zeiss
- Department of Comparative Medicine, Yale School of Medicine, New Haven, Connecticut 06520, USA
| | - Scott E Youlten
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut 06510, USA
| | - Smita Krishnaswamy
- Computational Biology and Bioinformatics Program, Yale University, New Haven, Connecticut 06511, USA;
- Yale Stem Cell Center, Yale School of Medicine, New Haven, Connecticut 06520, USA
- Department of Computer Science, Yale University, New Haven, Connecticut 06511, USA
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut 06510, USA
| | - Diane S Krause
- Yale Stem Cell Center, Yale School of Medicine, New Haven, Connecticut 06520, USA;
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut 06510, USA
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut 06520-8023, USA
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19
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Duong P, Rodriguez-Parks A, Kang J, Murphy PJ. CUT&Tag applied to zebrafish adult tail fins reveals a return of embryonic H3K4me3 patterns during regeneration. Epigenetics Chromatin 2024; 17:22. [PMID: 39033118 PMCID: PMC11264793 DOI: 10.1186/s13072-024-00547-5] [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: 03/29/2024] [Accepted: 07/10/2024] [Indexed: 07/23/2024] Open
Abstract
Regenerative potential is governed by a complex process of transcriptional reprogramming, involving chromatin reorganization and dynamics in transcription factor binding patterns throughout the genome. The degree to which chromatin and epigenetic changes contribute to this process remains only partially understood. Here we provide a modified CUT&Tag protocol suitable for improved characterization and interrogation of changes in chromatin modifications during adult fin regeneration in zebrafish. Our protocol generates data that recapitulates results from previously published ChIP-Seq methods, requires far fewer cells as input, and significantly improves signal to noise ratios. We deliver high-resolution enrichment maps for H3K4me3 of uninjured and regenerating fin tissues. During regeneration, we find that H3K4me3 levels increase over gene promoters which become transcriptionally active and genes which lose H3K4me3 become silenced. Interestingly, these reprogramming events recapitulate the H3K4me3 patterns observed in developing fin folds of 24-h old zebrafish embryos. Our results indicate that changes in genomic H3K4me3 patterns during fin regeneration occur in a manner consistent with reactivation of developmental programs, demonstrating CUT&Tag to be an effective tool for profiling chromatin landscapes in regenerating tissues.
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Affiliation(s)
- Phu Duong
- Department of Biomedical Genetics, University of Rochester, Rochester, USA
| | | | - Junsu Kang
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, USA.
| | - Patrick J Murphy
- Department of Biomedical Genetics, University of Rochester, Rochester, USA.
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20
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Wong GP, Hartmann S, Simmons DG, Ellis S, Nonn O, Cannon P, Nguyen TV, Nguyen A, Bartho LA, Tong S, Hannan NJ, Kaitu'u-Lino TJ. Trophoblast Side-Population Markers are Dysregulated in Preeclampsia and Fetal Growth Restriction. Stem Cell Rev Rep 2024:10.1007/s12015-024-10764-w. [PMID: 39028417 DOI: 10.1007/s12015-024-10764-w] [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] [Accepted: 07/10/2024] [Indexed: 07/20/2024]
Abstract
Dysregulated progenitor cell populations may contribute to poor placental development and placental insufficiency pathogenesis. Side-population cells possess progenitor properties. Recent human trophoblast side-population isolation identified enrichment of 8 specific genes (CXCL8, ELL2, GATA6, HK2, HLA-DPB1, INTS6, SERPINE3 and UPP1) (Gamage et al. 2020, Stem Cell Rev Rep). We characterised these trophoblast side-population markers in human placenta and in placental insufficiency disorders: preeclampsia and fetal growth restriction (FGR). Trophoblast side-population markers localised to mononuclear trophoblasts lining the placental villous basement membrane in preterm control, preeclamptic and FGR placental sections (n = 3, panel of 3 markers/serial section). Analysis of single-cell transcriptomics of an organoid human trophoblast stem cell (hTSC) to extravillous trophoblast (EVT) differentiation model (Shannon et al. 2022, Development) identified that all side-population genes were enriched in mononuclear trophoblast and trophoblasts committed to differentiation under hTSC culture conditions. In vitro validation via 96 h time course hTSC differentiation to EVTs or syncytiotrophoblasts (n = 5) demonstrated ELL2 and HK2 increased with differentiation (p < 0.0024, p < 0.0039 respectively). CXCL8 and HLA-DPB1 were downregulated (p < 0.030, p < 0.011 respectively). GATA6 and INTS6 increased with EVT differentiation only, and UPP1 reduced with syncytialisation. SERPINE3 was undetectable. Trophoblast side-population marker mRNA was measured in human placentas (< 34-weeks' gestation; n = 78 preeclampsia, n = 30 FGR, and n = 18 gestation-matched controls). ELL2, HK2 and CXCL8 were elevated in preeclamptic (p = 0.0006, p < 0.0001, p = 0.0335 respectively) and FGR placentas (p = 0.0065, p < 0.0001, p = 0.0001 respectively) versus controls. Placental GATA6 was reduced in pregnancies with preeclampsia and FGR (p = 0.0014, p = 0.0146 respectively). Placental INTS6 was reduced with FGR only (p < 0.0001). This study identified the localisation of a unique trophoblast subset enriched for side-population markers. Aberrant expression of some side-population markers may indicate disruptions to unique trophoblast subtypes in placental insufficiency.
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Affiliation(s)
- Georgia P Wong
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia.
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia.
| | - Sunhild Hartmann
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany
- Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Center for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - David G Simmons
- School of Biomedical Sciences, University of Queensland, Brisbane, Australia
| | - Sarah Ellis
- Olivia Newton-John Cancer Research Institute, Heidelberg, VIC, 3084, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Olivia Nonn
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany
- Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Center for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
| | - Ping Cannon
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Tuong-Vi Nguyen
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Anna Nguyen
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Lucy A Bartho
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Stephen Tong
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Natalie J Hannan
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Tu'uhevaha J Kaitu'u-Lino
- The Department of Obstetrics, Gynaecology and Newborn Health, Mercy Hospital for Women, University of Melbourne, 163 Studley Road, Heidelberg, Victoria, 3084, Australia
- Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
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21
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Rahman MF, Kurlovs AH, Vodnala M, Meibalan E, Means TK, Nouri N, de Rinaldis E, Savova V. Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603936. [PMID: 39071425 PMCID: PMC11275869 DOI: 10.1101/2024.07.17.603936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Immune-mediated diseases are characterized by aberrant immune responses, posing significant challenges to global health. In both inflammatory and autoimmune diseases, dysregulated immune reactions mediated by tissue-residing immune and non-immune cells precipitate chronic inflammation and tissue damage that is amplified by peripheral immune cell extravasation into the tissue. Chemokine receptors are pivotal in orchestrating immune cell migration, yet deciphering the signaling code across cell types, diseases and tissues remains an open challenge. To delineate disease-specific cell-cell communications involved in immune cell migration, we conducted a meta-analysis of publicly available single-cell RNA sequencing (scRNA-seq) data across diverse immune diseases and tissues. Our comprehensive analysis spanned multiple immune disorders affecting major organs: atopic dermatitis and psoriasis (skin), chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis (lung), ulcerative colitis (colon), IgA nephropathy and lupus nephritis (kidney). By interrogating ligand-receptor (L-R) interactions, alterations in cell proportions, and differential gene expression, we unveiled intricate disease-specific and common immune cell chemoattraction and extravasation patterns. Our findings delineate disease-specific L-R networks and shed light on shared immune responses across tissues and diseases. Insights gleaned from this analysis hold promise for the development of targeted therapeutics aimed at modulating immune cell migration to mitigate inflammation and tissue damage. This nuanced understanding of immune cell dynamics at the single-cell resolution opens avenues for precision medicine in immune disease management.
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Affiliation(s)
- Mouly F. Rahman
- Precision Medicine and Computational Biology, Sanofi US, Cambridge, MA 02141, United States
| | - Andre H. Kurlovs
- Precision Medicine and Computational Biology, Sanofi US, Cambridge, MA 02141, United States
| | - Munender Vodnala
- Precision Medicine and Computational Biology, Sanofi US, Cambridge, MA 02141, United States
| | - Elamaran Meibalan
- Precision Medicine and Computational Biology, Sanofi US, Cambridge, MA 02141, United States
| | - Terry K. Means
- Immunology & Inflammation Research Therapeutic Area, Sanofi US, Cambridge, MA 02141, United States
| | - Nima Nouri
- Precision Medicine and Computational Biology, Sanofi US, Cambridge, MA 02141, United States
| | - Emanuele de Rinaldis
- Precision Medicine and Computational Biology, Sanofi US, Cambridge, MA 02141, United States
| | - Virginia Savova
- Precision Medicine and Computational Biology, Sanofi US, Cambridge, MA 02141, United States
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22
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Tuong ZK, van der Merwe R, Canete PF, Roco JA. Computational estimation of clonal diversity in autoimmunity. Immunol Cell Biol 2024. [PMID: 39010261 DOI: 10.1111/imcb.12801] [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: 04/08/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/17/2024]
Abstract
Diversity is the cornerstone of the adaptive immune system, crucial for its effectiveness against constantly evolving pathogens that pose threats to higher vertebrates. Accurately measuring and interpreting this diversity presents challenges for immunologists, as changes in diversity and clonotype composition can tip the balance between protective immunity and autoimmunity. In this review, we present the current methods commonly used to measure diversity from single-cell T-cell receptor and B-cell receptor sequencing. We also discuss two case studies where single-cell sequencing and diversity estimations have led to breakthroughs in autoimmune disease discovery and therapeutic innovation, and reflect upon the necessity and importance of accurately defining and measuring lymphocyte diversity in these contexts.
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Affiliation(s)
- Zewen Kelvin Tuong
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Rohan van der Merwe
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Pablo F Canete
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Jonathan A Roco
- Biological Data Science Institute, College of Science, The Australian National University, Canberra, ACT, Australia
- Clinical Hub for Interventional Research, College of Health & Medicine, The Australian National University, Canberra, ACT, Australia
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23
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Amancherla K, Schlendorf KH, Chow N, Sheng Q, Freedman JE, Rathmell JC. Single-cell RNA-sequencing identifies unique cell-specific gene expression profiles in high-grade cardiac allograft vasculopathy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.10.602989. [PMID: 39026730 PMCID: PMC11257508 DOI: 10.1101/2024.07.10.602989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Background Cardiac allograft vasculopathy (CAV), a diffuse thickening of the intima of the coronary arteries and microvasculature, is the leading cause of late graft failure and mortality after heart transplantation (HT). Diagnosis involves invasive coronary angiography, which carries substantial risk, and minimally-invasive approaches to CAV diagnosis are urgently needed. Using single-cell RNA-sequencing in peripheral blood mononuclear cells (PBMCs), we sought to identify cell-specific gene expression profiles in CAV. Methods Whole blood was collected from 22 HT recipients with angiographically-confirmed CAV and 18 HT recipients without CAV. PBMCs were isolated and subjected to single-cell RNA-sequencing using a 10X Genomics microfluidic platform. Downstream analyses focused on differential expression of genes, cell compositional changes, and T cell receptor repertoire analyses. Results Across 40 PBMC samples, we isolated 134,984 cells spanning 8 major clusters and 31 subclusters of cell types. Compositional analyses showed subtle, but significant increases in CD4+ T central memory cells, and CD14+ and CD16+ monocytes in high-grade CAV (CAV-2 and CAV-3) as compared to low-grade or absent CAV. After adjusting for age, gender, and prednisone use, 745 genes were differentially expressed in a cell-specific manner in high-grade CAV. Weighted gene co-expression network analyses showed enrichment for putative pathways involved in inflammation and angiogenesis. There were no significant differences in T cell clonality or diversity with increasing CAV severity. Conclusions Unbiased whole transcriptomic analyses at single-cell resolution identify unique, cell-specific gene expression patterns in CAV, suggesting the potential utility of peripheral gene expression biomarkers in diagnosing CAV.
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24
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Nitz AA, Giraldez Chavez JH, Eliason ZG, Payne SH. Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses. J Proteome Res 2024. [PMID: 38981598 DOI: 10.1021/acs.jproteome.4c00091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.
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Affiliation(s)
- Alyssa A Nitz
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | | | - Zachary G Eliason
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
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25
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Qiao Y, Cheng T, Miao Z, Cui Y, Tu J. Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods. SMALL METHODS 2024:e2400789. [PMID: 38979872 DOI: 10.1002/smtd.202400789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Indexed: 07/10/2024]
Abstract
Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.
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Affiliation(s)
- Yi Qiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zikun Miao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yue Cui
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jing Tu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
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26
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Curion F, Rich-Griffin C, Agarwal D, Ouologuem S, Rue-Albrecht K, May L, Garcia GEL, Heumos L, Thomas T, Lason W, Sims D, Theis FJ, Dendrou CA. Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis. Genome Biol 2024; 25:181. [PMID: 38978088 PMCID: PMC11229213 DOI: 10.1186/s13059-024-03322-7] [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: 03/14/2023] [Accepted: 06/25/2024] [Indexed: 07/10/2024] Open
Abstract
Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.
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Affiliation(s)
- Fabiola Curion
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Charlotte Rich-Griffin
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Devika Agarwal
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Sarah Ouologuem
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Kevin Rue-Albrecht
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Lilly May
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Giulia E L Garcia
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Doctoral Training Centre, University of Oxford, Oxford, UK
| | - Lukas Heumos
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Comprehensive Pneumology Center With the CPC-M bioArchive, Helmholtz Zentrum Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Tom Thomas
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Wojciech Lason
- Nuffield Department of Medicine, Respiratory Medicine Unit, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - David Sims
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Fabian J Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
| | - Calliope A Dendrou
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK.
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
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27
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Li C, Thijssen J, Kroes T, van der Burg X, van der Weerd L, Höllt T, Lelieveldt B. GeneSurfer Enables Transcriptome-wide Exploration and Functional Annotation of Gene Co-expression Modules in 3D Spatial Transcriptomics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.05.602230. [PMID: 39005368 PMCID: PMC11245099 DOI: 10.1101/2024.07.05.602230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Gene co-expression provides crucial insights into biological functions, however, there is a lack of exploratory analysis tools for localized gene co-expression in large-scale datasets. We present GeneSurfer, an interactive interface designed to explore localized transcriptome-wide gene co-expression patterns in the 3D spatial domain. Key features of GeneSurfer include transcriptome-wide gene filtering and gene clustering based on spatial local co-expression within transcriptomically similar cells, multi-slice 3D rendering of average expression of gene clusters, and on-the-fly Gene Ontology term annotation of co-expressed gene sets. Additionally, GeneSurfer offers multiple linked views for investigating individual genes or gene co-expression in the spatial domain at each exploration stage. Demonstrating its utility with both spatial transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas, GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression, providing biological insights and facilitating hypothesis generation and validation.
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Affiliation(s)
- Chang Li
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Julian Thijssen
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Thomas Kroes
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Ximaine van der Burg
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Louise van der Weerd
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Thomas Höllt
- Computer Graphics and Visualization, INSY, TU Delft, 2628 XE Delft, The Netherlands
| | - Boudewijn Lelieveldt
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Delft Bioinformatics Lab, INSY, TU Delft, 2628 XE Delft, The Netherlands
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28
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Chari T, Gorin G, Pachter L. Stochastic Modeling of Biophysical Responses to Perturbation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602131. [PMID: 39005347 PMCID: PMC11245117 DOI: 10.1101/2024.07.04.602131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the 'how' behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.
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Affiliation(s)
- Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
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29
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Engel A, Rishik S, Hirsch P, Keller V, Fehlmann T, Kern F, Keller A. SingmiR: a single-cell miRNA alignment and analysis tool. Nucleic Acids Res 2024; 52:W374-W380. [PMID: 38572750 PMCID: PMC11223861 DOI: 10.1093/nar/gkae225] [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: 02/19/2024] [Revised: 03/08/2024] [Accepted: 04/02/2024] [Indexed: 04/05/2024] Open
Abstract
Single-cell RNA sequencing (RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login at https://www.ccb.uni-saarland.de/singmir.
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Affiliation(s)
- Annika Engel
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Shusruto Rishik
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Pascal Hirsch
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Verena Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Fabian Kern
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
- Department of Clinical Bioinformatics (CLIB), Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
- Department of Clinical Bioinformatics (CLIB), Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany
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30
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Kaag Rasmussen M, Møllgård K, Bork PAR, Weikop P, Esmail T, Drici L, Wewer Albrechtsen NJ, Carlsen JF, Huynh NPT, Ghitani N, Mann M, Goldman SA, Mori Y, Chesler AT, Nedergaard M. Trigeminal ganglion neurons are directly activated by influx of CSF solutes in a migraine model. Science 2024; 385:80-86. [PMID: 38963846 DOI: 10.1126/science.adl0544] [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: 09/27/2023] [Accepted: 05/01/2024] [Indexed: 07/06/2024]
Abstract
Classical migraine patients experience aura, which is transient neurological deficits associated with cortical spreading depression (CSD), preceding headache attacks. It is not currently understood how a pathological event in cortex can affect peripheral sensory neurons. In this study, we show that cerebrospinal fluid (CSF) flows into the trigeminal ganglion, establishing nonsynaptic signaling between brain and trigeminal cells. After CSD, ~11% of the CSF proteome is altered, with up-regulation of proteins that directly activate receptors in the trigeminal ganglion. CSF collected from animals exposed to CSD activates trigeminal neurons in naïve mice in part by CSF-borne calcitonin gene-related peptide (CGRP). We identify a communication pathway between the central and peripheral nervous system that might explain the relationship between migrainous aura and headache.
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Affiliation(s)
- Martin Kaag Rasmussen
- Center for Translational Neuromedicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Kjeld Møllgård
- Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Peter A R Bork
- Center for Translational Neuromedicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Pia Weikop
- Center for Translational Neuromedicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Tina Esmail
- Center for Translational Neuromedicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Lylia Drici
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Nicolai J Wewer Albrechtsen
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Department for Clinical Biochemistry, University Hospital Copenhagen - Bispebjerg, Copenhagen, 2400 Copenhagen, Denmark
| | - Jonathan Frederik Carlsen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Department of Radiology, Copenhagen University Hospital-Rigshospitalet, 2100 Copenhagen, Denmark
| | - Nguyen P T Huynh
- Center for Translational Neuromedicine, Division of Glial Disease and Therapeutics, University of Rochester Medical Center, Rochester, NY 14642, USA
- Sana Biotechnology, Cambridge, MA 02139, USA
| | - Nima Ghitani
- National Center for Complementary and Integrative Health (NCCIH), Bethesda, MD 20892, USA
| | - Matthias Mann
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Steven A Goldman
- Center for Translational Neuromedicine, University of Copenhagen, 2200 Copenhagen, Denmark
- Center for Translational Neuromedicine, Division of Glial Disease and Therapeutics, University of Rochester Medical Center, Rochester, NY 14642, USA
- Sana Biotechnology, Cambridge, MA 02139, USA
| | - Yuki Mori
- Center for Translational Neuromedicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Alexander T Chesler
- National Center for Complementary and Integrative Health (NCCIH), Bethesda, MD 20892, USA
- National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA
| | - Maiken Nedergaard
- Center for Translational Neuromedicine, University of Copenhagen, 2200 Copenhagen, Denmark
- Center for Translational Neuromedicine, Division of Glial Disease and Therapeutics, University of Rochester Medical Center, Rochester, NY 14642, USA
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31
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Udugampolage NS, Frolova S, Taurino J, Pini A, Martelli F, Voellenkle C. Coding and Non-Coding Transcriptomic Landscape of Aortic Complications in Marfan Syndrome. Int J Mol Sci 2024; 25:7367. [PMID: 39000474 PMCID: PMC11242319 DOI: 10.3390/ijms25137367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
Marfan syndrome (MFS) is a rare congenital disorder of the connective tissue, leading to thoracic aortic aneurysms (TAA) and dissection, among other complications. Currently, the most efficient strategy to prevent life-threatening dissection is preventive surgery. Periodic imaging applying complex techniques is required to monitor TAA progression and to guide the timing of surgical intervention. Thus, there is an acute demand for non-invasive biomarkers for diagnosis and prognosis, as well as for innovative therapeutic targets of MFS. Unraveling the intricate pathomolecular mechanisms underlying the syndrome is vital to address these needs. High-throughput platforms are particularly well-suited for this purpose, as they enable the integration of different datasets, such as transcriptomic and epigenetic profiles. In this narrative review, we summarize relevant studies investigating changes in both the coding and non-coding transcriptome and epigenome in MFS-induced TAA. The collective findings highlight the implicated pathways, such as TGF-β signaling, extracellular matrix structure, inflammation, and mitochondrial dysfunction. Potential candidates as biomarkers, such as miR-200c, as well as therapeutic targets emerged, like Tfam, associated with mitochondrial respiration, or miR-632, stimulating endothelial-to-mesenchymal transition. While these discoveries are promising, rigorous and extensive validation in large patient cohorts is indispensable to confirm their clinical relevance and therapeutic potential.
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Affiliation(s)
| | - Svetlana Frolova
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, 20097 Milan, Italy; (S.F.); (C.V.)
- Department of Biosciences, University of Milan, 20122 Milan, Italy
| | - Jacopo Taurino
- Cardiovascular-Genetic Center, IRCCS Policlinico San Donato, 20097 Milan, Italy; (N.S.U.); (J.T.); (A.P.)
| | - Alessandro Pini
- Cardiovascular-Genetic Center, IRCCS Policlinico San Donato, 20097 Milan, Italy; (N.S.U.); (J.T.); (A.P.)
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, 20097 Milan, Italy; (S.F.); (C.V.)
| | - Christine Voellenkle
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, 20097 Milan, Italy; (S.F.); (C.V.)
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Drost F, An Y, Bonafonte-Pardàs I, Dratva LM, Lindeboom RGH, Haniffa M, Teichmann SA, Theis F, Lotfollahi M, Schubert B. Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data. Nat Commun 2024; 15:5577. [PMID: 38956082 PMCID: PMC11220149 DOI: 10.1038/s41467-024-49806-9] [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: 10/10/2023] [Accepted: 05/23/2024] [Indexed: 07/04/2024] Open
Abstract
Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses.
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Affiliation(s)
- Felix Drost
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354, Freising, Germany
| | - Yang An
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748, Garching bei München, Germany
| | - Irene Bonafonte-Pardàs
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Lisa M Dratva
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Rik G H Lindeboom
- The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Department of Physics, Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge, UK
| | - Fabian Theis
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354, Freising, Germany
- School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748, Garching bei München, Germany
| | - Mohammad Lotfollahi
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
| | - Benjamin Schubert
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748, Garching bei München, Germany.
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Qadir MMF, Elgamal RM, Song K, Kudtarkar P, Sakamuri SS, Katakam PV, El-Dahr SS, Kolls JK, Gaulton KJ, Mauvais-Jarvis F. Single cell regulatory architecture of human pancreatic islets suggests sex differences in β cell function and the pathogenesis of type 2 diabetes. RESEARCH SQUARE 2024:rs.3.rs-4607352. [PMID: 39011095 PMCID: PMC11247939 DOI: 10.21203/rs.3.rs-4607352/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Type 2 and type 1 diabetes (T2D, T1D) exhibit sex differences in insulin secretion, the mechanisms of which are unknown. We examined sex differences in human pancreatic islets from 52 donors with and without T2D combining single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), hormone secretion, and bioenergetics. In nondiabetic (ND) donors, sex differences in islet cells gene accessibility and expression predominantly involved sex chromosomes. Islets from T2D donors exhibited similar sex differences in sex chromosomes differentially expressed genes (DEGs), but also exhibited sex differences in autosomal genes. Comparing β cells from T2D vs. ND donors, gene enrichment of female β cells showed suppression in mitochondrial respiration, while male β cells exhibited suppressed insulin secretion. Thus, although sex differences in gene accessibility and expression of ND β cells predominantly affect sex chromosomes, the transition to T2D reveals sex differences in autosomes highlighting mitochondrial failure in females.
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Affiliation(s)
- Mirza Muhammad Fahd Qadir
- Section of Endocrinology and Metabolism, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, USA
- Tulane Center of Excellence in Sex-Based Biology & Medicine, New Orleans, LA, USA
| | - Ruth M. Elgamal
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Keijing Song
- Center for Translational Research in Infection and Inflammation, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Siva S.V.P Sakamuri
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Prasad V. Katakam
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Samir S. El-Dahr
- Department of Pediatrics, Tulane University, School of Medicine, New Orleans, LA, USA
| | - Jay K. Kolls
- Center for Translational Research in Infection and Inflammation, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Franck Mauvais-Jarvis
- Section of Endocrinology and Metabolism, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, USA
- Tulane Center of Excellence in Sex-Based Biology & Medicine, New Orleans, LA, USA
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34
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Rhaman MS, Ali M, Ye W, Li B. Opportunities and Challenges in Advancing Plant Research with Single-cell Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae026. [PMID: 38996445 DOI: 10.1093/gpbjnl/qzae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 07/14/2024]
Abstract
Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.
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Affiliation(s)
- Mohammad Saidur Rhaman
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Muhammad Ali
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Wenxiu Ye
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Bosheng Li
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
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Peeters F, Cappuyns S, Piqué-Gili M, Phillips G, Verslype C, Lambrechts D, Dekervel J. Applications of single-cell multi-omics in liver cancer. JHEP Rep 2024; 6:101094. [PMID: 39022385 PMCID: PMC11252522 DOI: 10.1016/j.jhepr.2024.101094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/18/2024] [Accepted: 03/27/2024] [Indexed: 07/20/2024] Open
Abstract
Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.
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Affiliation(s)
- Frederik Peeters
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Sarah Cappuyns
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Marta Piqué-Gili
- Liver Cancer Translational Research Laboratory, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Gino Phillips
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Chris Verslype
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Jeroen Dekervel
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
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36
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [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: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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37
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Weiler P, Lange M, Klein M, Pe'er D, Theis F. CellRank 2: unified fate mapping in multiview single-cell data. Nat Methods 2024; 21:1196-1205. [PMID: 38871986 PMCID: PMC11239496 DOI: 10.1038/s41592-024-02303-9] [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: 07/18/2023] [Accepted: 05/09/2024] [Indexed: 06/15/2024]
Abstract
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
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Affiliation(s)
- Philipp Weiler
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Marius Lange
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Michal Klein
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Machine Learning Research, Apple, Paris, France
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Fabian Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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38
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Ma C, Hao Y, Shi B, Wu Z, Jin D, Yu X, Jin B. Unveiling mitochondrial and ribosomal gene deregulation and tumor microenvironment dynamics in acute myeloid leukemia. Cancer Gene Ther 2024; 31:1034-1048. [PMID: 38806621 DOI: 10.1038/s41417-024-00788-2] [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: 01/10/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 05/30/2024]
Abstract
Acute myeloid leukemia (AML) is a malignant clonal hematopoietic disease with a poor prognosis. Understanding the interaction between leukemic cells and the tumor microenvironment (TME) can help predict the prognosis of leukemia and guide its treatment. Re-analyzing the scRNA-seq data from the CSC and G20 cohorts, using a Python-based pipeline including machine-learning-based scVI-tools, recapitulated the distinct hierarchical structure within the samples of AML patients. Weighted correlation network analysis (WGCNA) was conducted to construct a weighted gene co-expression network and to identify gene modules primarily focusing on hematopoietic stem cells (HSCs), multipotent progenitors (MPPs), and natural killer (NK) cells. The analysis revealed significant deregulation in gene modules associated with aerobic respiration and ribosomal/cytoplasmic translation. Cell-cell communications were elucidated by the CellChat package, revealing an imbalance of activating and inhibitory immune signaling pathways. Interception of genes upregulated in leukemic HSCs & MPPs as well as in NKG2A-high NK cells was used to construct prognostic models. Normal Cox and artificial neural network models based on 10 genes were developed. The study reveals the deregulation of mitochondrial and ribosomal genes in AML patients and suggests the co-occurrence of stimulatory and inhibitory factors in the AML TME.
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Affiliation(s)
- Chao Ma
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Yuchao Hao
- Department of Hematology, The Second Hospital of Dalian Medical University, West Section Lvshun South Road, Dalian, 116027, Liaoning, China
| | - Bo Shi
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Zheng Wu
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Di Jin
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Xiao Yu
- NHC Key Laboratory of Pneumoconiosis, The First Hospital of Shanxi Medical University, South Jiefang Road, Taiyuan, 030001, Shanxi, China.
| | - Bilian Jin
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China.
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Francis L, Capon F, Smith CH, Haniffa M, Mahil SK. Inflammatory memory in psoriasis: From remission to recurrence. J Allergy Clin Immunol 2024; 154:42-50. [PMID: 38761994 DOI: 10.1016/j.jaci.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
The routine use of targeted systemic immunomodulatory therapies has transformed outcomes for people with severe psoriasis, with skin clearance (clinical remission) rates up to 60% at 1 year of biologic treatment. However, psoriasis may recur following drug withdrawal, and as a result, patients tend to continue receiving costly treatment indefinitely. Here, we review research into the "inflammatory memory" in resolved psoriasis skin and the potential mechanisms leading to psoriasis recurrence following drug withdrawal. Research has implicated immune cells such as tissue resident memory T cells, Langerhans cells, and dermal dendritic cells, and there is growing interest in keratinocytes and fibroblasts. A better understanding of the interactions between these cell populations, enabled by single cell technologies, will help to elucidate the events underpinning the shift from remission to recurrence. This may inform the development of personalized strategies for sustaining remission while reducing long-term drug burden.
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Affiliation(s)
- Luc Francis
- St John's Institute of Dermatology, King's College London and Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Francesca Capon
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Catherine H Smith
- St John's Institute of Dermatology, King's College London and Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, United Kingdom; Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Satveer K Mahil
- St John's Institute of Dermatology, King's College London and Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom.
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40
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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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41
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Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2024; 42:1133-1149. [PMID: 37679543 PMCID: PMC11252085 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
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Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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42
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MaxFuse enables data integration across weakly linked spatial and single-cell modalities. Nat Biotechnol 2024; 42:1036-1037. [PMID: 37679547 DOI: 10.1038/s41587-023-01943-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
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43
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Chen EZ, Kannan S, Murphy S, Farid M, Kwon C. Protocol for quantifying stem-cell-derived cardiomyocyte maturity using transcriptomic entropy score. STAR Protoc 2024; 5:103083. [PMID: 38781077 PMCID: PMC11145390 DOI: 10.1016/j.xpro.2024.103083] [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: 12/08/2023] [Revised: 03/15/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
The inability to quantify cardiomyocyte (CM) maturation remains a significant barrier to evaluating the effects of ongoing efforts to produce adult-like CMs from pluripotent stem cells (PSCs). Here, we present a protocol to quantify stem-cell-derived CM maturity using a single-cell RNA sequencing-based metric "entropy score." We describe steps for generating an entropy score using customized R code. This tool can be used to quantify maturation levels of PSC-CMs and potentially other cell types. For complete details on the use and execution of this protocol, please refer to Kannan et al.1.
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Affiliation(s)
- Elaine Zhelan Chen
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Suraj Kannan
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Sean Murphy
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Michael Farid
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Chulan Kwon
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA.
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44
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Nakamura J, Shiohama Y, Röth D, Haruta T, Yamashita Y, Mitsuzono T, Mochizuki C, Kalkum M, Nakamura M. Size and Surface Properties of Functionalized Organosilica Particles Impact Cell-Particle Interactions Including Mitochondrial Activity. ACS APPLIED MATERIALS & INTERFACES 2024; 16:30980-30996. [PMID: 38857433 DOI: 10.1021/acsami.4c06455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Understanding of the interactions between macrophages and multifunctional nanoparticles is important for development of novel macrophage-based immunotherapies. Here, we investigated the effects of fluorescent thiol-organosilica particle size and surface properties on cell-particle interactions, including mitochondrial activity, using the mouse macrophage cell line J774A.1. Three different sizes of thiol-organosilica particles (150, 400, and 680 nm in diameter) containing fluorescein (OS/F150, OS/F400, and OS/F680) and particles surface functionalized with polyethylenimine (PEI) (OS/F150PEI, OS/F400PEI, and OS/F680PEI) were prepared. Flow cytometric analysis, time-lapse imaging, and single-cell analysis of particle uptake and mitochondrial activity of J774A.1 cells demonstrated variations in uptake and kinetics depending on the particle size and surface as well as on each individual cell. Cells treated with OS/F150 and OS/F150PEI showed higher uptake and mitochondrial activity than those treated with other particles. The interaction between endosomes and mitochondria was observed using 3D fluorescent imaging and was characterized by the involvement of iron transport into mitochondria by iron-containing proteins adsorbed on the particle surface. Scanning electron microscopy of the cells treated with the particles revealed alterations in cell membrane morphology, depending on particle size and surface. We performed correlative light and electron microscopy combined with time-lapse and 3D imaging to develop an integrated correlation analysis of particle uptake, mitochondrial activity, and cell membrane morphology in single macrophages. These cell-specific characteristics of macrophages against functional particles and their evaluation methods are crucial for understanding the immunological functions of individual macrophages and developing novel immunotherapies.
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Affiliation(s)
- Junna Nakamura
- Department of Organ Anatomy and Nanomedicine, Yamaguchi University Graduate School of Medicine, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Core Clusters for Research Initiatives of Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Research Institute for Cell Design Medical Science, Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Yasuo Shiohama
- Department of Organ Anatomy and Nanomedicine, Yamaguchi University Graduate School of Medicine, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Core Clusters for Research Initiatives of Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Research Institute for Cell Design Medical Science, Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Daniel Röth
- Department of Department of Immunology & Theranostics, Arthur Riggs Diabetes & Metabolism Research Institute, Beckman Research Institute, City of Hope, Duarte, California 91010, United States
| | - Tomohiro Haruta
- EM application group, EM business unit, JEOL Ltd., Akishima, Tokyo JP 196-8558, Japan
| | - Yukari Yamashita
- Department of Organ Anatomy and Nanomedicine, School of Medicine, Facuelty of Medicine and Health Sciences, Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Tomohiro Mitsuzono
- Department of Organ Anatomy and Nanomedicine, School of Medicine, Facuelty of Medicine and Health Sciences, Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Chihiro Mochizuki
- Department of Organ Anatomy and Nanomedicine, Yamaguchi University Graduate School of Medicine, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Core Clusters for Research Initiatives of Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Research Institute for Cell Design Medical Science, Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Markus Kalkum
- Department of Department of Immunology & Theranostics, Arthur Riggs Diabetes & Metabolism Research Institute, Beckman Research Institute, City of Hope, Duarte, California 91010, United States
| | - Michihiro Nakamura
- Department of Organ Anatomy and Nanomedicine, Yamaguchi University Graduate School of Medicine, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Core Clusters for Research Initiatives of Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
- Research Institute for Cell Design Medical Science, Yamaguchi University, 1-1-1 minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
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Regner MJ, Garcia-Recio S, Thennavan A, Wisniewska K, Mendez-Giraldez R, Felsheim B, Spanheimer PM, Parker JS, Perou CM, Franco HL. Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598858. [PMID: 38948758 PMCID: PMC11212881 DOI: 10.1101/2024.06.13.598858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.
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Affiliation(s)
- Matthew J. Regner
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Aatish Thennavan
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX, USA, 77030
| | - Kamila Wisniewska
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Raul Mendez-Giraldez
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Brooke Felsheim
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Philip M. Spanheimer
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Joel S. Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hector L. Franco
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Division of Clinical and Translational Cancer Research, University of Puerto Rico Comprehensive Cancer Center, San Juan, PR 00935
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46
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Morin A, Chu C, Pavlidis P. Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580581. [PMID: 38559016 PMCID: PMC10979919 DOI: 10.1101/2024.02.15.580581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes, rather than cell type compositional effects that emerge from bulk tissue samples. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.
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Affiliation(s)
- Alexander Morin
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Chingpan Chu
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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47
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Atta L, Clifton K, Anant M, Aihara G, Fan J. Gene count normalization in single-cell imaging-based spatially resolved transcriptomics. Genome Biol 2024; 25:153. [PMID: 38867267 PMCID: PMC11167774 DOI: 10.1186/s13059-024-03303-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: 08/18/2023] [Accepted: 06/06/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. RESULTS Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. CONCLUSIONS We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.
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Affiliation(s)
- Lyla Atta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, USA
| | - Kalen Clifton
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, USA
| | - Manjari Anant
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Gohta Aihara
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, USA.
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Finlay JB, Ireland AS, Hawgood SB, Reyes T, Ko T, Olsen RR, Abi Hachem R, Jang DW, Bell D, Chan JM, Goldstein BJ, Oliver TG. Olfactory neuroblastoma mimics molecular heterogeneity and lineage trajectories of small-cell lung cancer. Cancer Cell 2024; 42:1086-1105.e13. [PMID: 38788720 PMCID: PMC11186085 DOI: 10.1016/j.ccell.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/13/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
The olfactory epithelium undergoes neuronal regeneration from basal stem cells and is susceptible to olfactory neuroblastoma (ONB), a rare tumor of unclear origins. Employing alterations in Rb1/Trp53/Myc (RPM), we establish a genetically engineered mouse model of high-grade metastatic ONB exhibiting a NEUROD1+ immature neuronal phenotype. We demonstrate that globose basal cells (GBCs) are a permissive cell of origin for ONB and that ONBs exhibit cell fate heterogeneity that mimics normal GBC developmental trajectories. ASCL1 loss in RPM ONB leads to emergence of non-neuronal histopathologies, including a POU2F3+ microvillar-like state. Similar to small-cell lung cancer (SCLC), mouse and human ONBs exhibit mutually exclusive NEUROD1 and POU2F3-like states, an immune-cold tumor microenvironment, intratumoral cell fate heterogeneity comprising neuronal and non-neuronal lineages, and cell fate plasticity-evidenced by barcode-based lineage tracing and single-cell transcriptomics. Collectively, our findings highlight conserved similarities between ONB and neuroendocrine tumors with significant implications for ONB classification and treatment.
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Affiliation(s)
- John B Finlay
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Abbie S Ireland
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA
| | - Sarah B Hawgood
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA
| | - Tony Reyes
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA; Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA
| | - Tiffany Ko
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Rachelle R Olsen
- Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA
| | - Ralph Abi Hachem
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - David W Jang
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Diana Bell
- Division of Anatomic Pathology, City of Hope Comprehensive Cancer Center, Duarte 91010, CA, USA
| | - Joseph M Chan
- Human Oncology and Pathogenesis Program, Memorial-Sloan Kettering Cancer Center, New York City 10065, NY, USA
| | - Bradley J Goldstein
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA; Department of Neurobiology, Duke University, Durham 27710, NC, USA.
| | - Trudy G Oliver
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA; Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA.
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Totty M, Hicks SC, Guo B. SpotSweeper: spatially-aware quality control for spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597765. [PMID: 38895212 PMCID: PMC11185656 DOI: 10.1101/2024.06.06.597765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Quality control (QC) is a crucial step to ensure the reliability and accuracy of the data obtained from RNA sequencing experiments, including spatially-resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-nucleus RNA sequencing (snRNA-seq) methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts unique to SRT. Here, we introduce SpotSweeper, spatially-aware QC methods for identifying local outliers and regional artifacts in SRT. SpotSweeper evaluates the quality of individual spots relative to their local neighborhood, thus minimizing bias due to biological heterogeneity, and uses multiscale methods to detect regional artifacts. Using SpotSweeper on publicly available data, we identified a consistent set of Visium barcodes/spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts, resulting in improved downstream clustering and marker gene detection for spatial domains.
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Affiliation(s)
- Michael Totty
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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50
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Palmer JA, Rosenthal N, Teichmann SA, Litvinukova M. Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics. Circ Res 2024; 134:1681-1702. [PMID: 38843288 PMCID: PMC11149945 DOI: 10.1161/circresaha.124.323672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.
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Affiliation(s)
- Jack A. Palmer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom (J.A.P., S.A.T.)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus (J.A.P., S.A.T.), University of Cambridge, United Kingdom
| | - Nadia Rosenthal
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME (N.R.)
- National Heart and Lung Institute, Imperial College London, United Kingdom (N.R.)
| | - Sarah A. Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom (J.A.P., S.A.T.)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus (J.A.P., S.A.T.), University of Cambridge, United Kingdom
- Theory of Condensed Matter Group, Department of Physics, Cavendish Laboratory (S.A.T.), University of Cambridge, United Kingdom
| | - Monika Litvinukova
- University Hospital Würzburg, Germany (M.L.)
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Germany (M.L.)
- Helmholtz Pioneer Campus, Helmholtz Munich, Germany (M.L.)
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