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Article ; Online: Cell Atlases as Roadmaps in Health and Disease.

Regev, Aviv

2020 Volume 69, Issue 4, Page(s) 105

Abstract: The recent advent of methods for high-throughput single-cell and spatial profiling have opened the way to complete the 150-year-old endeavor of identifying all cell types in the human body, by their distinctive molecular profiles, and to relate this ... ...

Abstract	The recent advent of methods for high-throughput single-cell and spatial profiling have opened the way to complete the 150-year-old endeavor of identifying all cell types in the human body, by their distinctive molecular profiles, and to relate this information to other cellular descriptions, physiological phenotypes, molecular mechanisms and functions. Our effort to build a comprehensive reference map of the molecular state of cells in healthy human tissues is propelling the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, provides a framework for understanding cellular dysregulation in human disease, and suggests the possibility of predicting cell types and behaviors, towards a "periodic table of our cells". In this talk, I describe our foundational work underlying single cell genomics and the conceptual framework and impact of our understanding of cell and tissue biology in health, as well as how we use it to shed light on rare disease, cancer, and COVID-19.
MeSH term(s)	Atlases as Topic ; Cell Biology ; Genomics ; Humans ; Single-Cell Analysis
Language	English
Publishing date	2020-12-31
Publishing country	Japan
Document type	Journal Article
ZDB-ID	390981-5
ISSN	1880-1293 ; 0022-9717
ISSN (online)	1880-1293
ISSN	0022-9717
DOI	10.2302/kjm.69-002-ABST
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Article ; Online: DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data.

Jerby-Arnon, Livnat / Regev, Aviv

Nature biotechnology

2022 Volume 40, Issue 10, Page(s) 1467–1477

Abstract: Deciphering the functional interactions of cells in tissues remains a major challenge. Here we describe DIALOGUE, a method to systematically uncover multicellular programs (MCPs)-combinations of coordinated cellular programs in different cell types that ... ...

Abstract	Deciphering the functional interactions of cells in tissues remains a major challenge. Here we describe DIALOGUE, a method to systematically uncover multicellular programs (MCPs)-combinations of coordinated cellular programs in different cell types that form higher-order functional units at the tissue level-from either spatial data or single-cell data obtained without spatial information. Tested on spatial datasets from the mouse hypothalamus, cerebellum, visual cortex and neocortex, DIALOGUE identified MCPs associated with animal behavior and recovered spatial properties when tested on unseen data while outperforming other methods and metrics. In spatial data from human lung cancer, DIALOGUE identified MCPs marking immune activation and tissue remodeling. Applied to single-cell RNA sequencing data across individuals or regions, DIALOGUE uncovered MCPs marking Alzheimer's disease, ulcerative colitis and resistance to cancer immunotherapy. These programs were predictive of disease outcome and predisposition in independent cohorts and included risk genes from genome-wide association studies. DIALOGUE enables the analysis of multicellular regulation in health and disease.
MeSH term(s)	Alzheimer Disease/genetics ; Animals ; Genome-Wide Association Study ; Humans ; Mice ; Single-Cell Analysis ; Transcriptome/genetics
Language	English
Publishing date	2022-05-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-022-01288-0
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Zs.A 2167: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 137: Show issues
Zs.MG 43: Show issues

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Article ; Online: Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces.

Ding, Jiarui / Regev, Aviv

Nature communications

2021 Volume 12, Issue 1, Page(s) 2554

Abstract: Single-cell RNA-Seq (scRNA-seq) is invaluable for studying biological systems. Dimensionality reduction is a crucial step in interpreting the relation between cells in scRNA-seq data. However, current dimensionality reduction methods are often confounded ...

Abstract	Single-cell RNA-Seq (scRNA-seq) is invaluable for studying biological systems. Dimensionality reduction is a crucial step in interpreting the relation between cells in scRNA-seq data. However, current dimensionality reduction methods are often confounded by multiple simultaneous technical and biological variability, result in "crowding" of cells in the center of the latent space, or inadequately capture temporal relationships. Here, we introduce scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces to accurately represent scRNA-seq data. ScPhere addresses multi-level, complex batch factors, facilitates the interactive visualization of large datasets, resolves cell crowding, and uncovers temporal trajectories. We demonstrate scPhere on nine large datasets in complex tissue from human patients or animal development. Our results show how scPhere facilitates the interpretation of scRNA-seq data by generating batch-invariant embeddings to map data from new individuals, identifies cell types affected by biological variables, infers cells' spatial positions in pre-defined biological specimens, and highlights complex cellular relations.
MeSH term(s)	Animals ; Colon ; Computational Biology/methods ; Epithelial Cells ; Gene Expression Profiling/methods ; Humans ; Machine Learning ; RNA-Seq/methods ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods
Language	English
Publishing date	2021-05-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-22851-4
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Article ; Online: The legacy of the Human Genome Project.

Rood, Jennifer E / Regev, Aviv

Science (New York, N.Y.)

2021 Volume 373, Issue 6562, Page(s) 1442–1443

Abstract: The initiative forever altered biomedicine, but work remains to fulfill its true potential. ...

Abstract	The initiative forever altered biomedicine, but work remains to fulfill its true potential.
MeSH term(s)	Computational Biology ; Databases, Genetic ; Genome, Human ; Genome-Wide Association Study ; Genomics ; Health ; Human Genetics ; Human Genome Project ; Humans ; Information Dissemination ; Neoplasms/genetics ; Population Groups/genetics ; Rare Diseases/genetics
Language	English
Publishing date	2021-09-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	128410-1
ISSN	1095-9203 ; 0036-8075
ISSN (online)	1095-9203
ISSN	0036-8075
DOI	10.1126/science.abl5403
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Zs.A 27: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 77: Show issues

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Article ; Online: Toward the Identifiability of Comparative Deep Generative Models.

Lopez, Romain / Huetter, Jan-Christian / Hajiramezanali, Ehsan / Pritchard, Jonathan / Regev, Aviv

ArXiv

2024

Abstract: Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of ... ...

Abstract	Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of comparing data sets from different sources. One such example is the setting of contrastive analysis that focuses on describing patterns that are enriched in a target data set compared to a background data set. The practical deployment of those models often assumes that DGMs naturally infer interpretable and modular latent representations, which is known to be an issue in practice. Consequently, existing methods often rely on ad-hoc regularization schemes, although without any theoretical grounding. Here, we propose a theory of identifiability for comparative DGMs by extending recent advances in the field of non-linear independent component analysis. We show that, while these models lack identifiability across a general class of mixing functions, they surprisingly become identifiable when the mixing function is piece-wise affine (e.g., parameterized by a ReLU neural network). We also investigate the impact of model misspecification, and empirically show that previously proposed regularization techniques for fitting comparative DGMs help with identifiability when the number of latent variables is not known in advance. Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization. We empirically validate our theory and new methodology using simulated data as well as a recent data set of genetic perturbations in cells profiled via single-cell RNA sequencing.
Language	English
Publishing date	2024-01-29
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: Spatial host-microbiome sequencing reveals niches in the mouse gut.

Lötstedt, Britta / Stražar, Martin / Xavier, Ramnik / Regev, Aviv / Vickovic, Sanja

Nature biotechnology

2023

Abstract: Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high ... ...

Abstract	Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host-microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host-bacteria interactions. SHM-seq should enhance the study of native host-microbe interactions in health and disease.
Language	English
Publishing date	2023-11-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-023-01988-1
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Zs.A 2167: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 137: Show issues
Zs.MG 43: Show issues

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Article ; Online: The network effect: studying COVID-19 pathology with the Human Cell Atlas.

Teichmann, Sarah / Regev, Aviv

Nature reviews. Molecular cell biology

2020 Volume 21, Issue 8, Page(s) 415–416

MeSH term(s)	Angiotensin-Converting Enzyme 2 ; COVID-19 ; Cells/pathology ; Coronavirus Infections/pathology ; Coronavirus Infections/therapy ; Coronavirus Infections/transmission ; Humans ; Meta-Analysis as Topic ; Organ Specificity ; Pandemics ; Peptidyl-Dipeptidase A/metabolism ; Pneumonia, Viral/pathology ; Pneumonia, Viral/therapy ; Pneumonia, Viral/transmission ; Single-Cell Analysis
Chemical Substances	Peptidyl-Dipeptidase A (EC 3.4.15.1) ; ACE2 protein, human (EC 3.4.17.23) ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23)
Keywords	covid19
Language	English
Publishing date	2020-06-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2031313-5
ISSN	1471-0080 ; 1471-0072
ISSN (online)	1471-0080
ISSN	1471-0072
DOI	10.1038/s41580-020-0267-3
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Zs.A 5446: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 26: Show issues

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Article ; Online: Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq.

Fishman, Lior / Modak, Avani / Nechooshtan, Gal / Razin, Talya / Erhard, Florian / Regev, Aviv / Farrell, Jeffrey A / Rabani, Michal

Nature communications

2024 Volume 15, Issue 1, Page(s) 3104

Abstract: During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles ... ...

Abstract	During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities. Here, we combine single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types. Our study provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.
MeSH term(s)	Animals ; Zebrafish ; Single-Cell Gene Expression Analysis ; Embryonic Development/genetics ; Transcription, Genetic ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Gene Expression Regulation, Developmental
Chemical Substances	RNA, Messenger
Language	English
Publishing date	2024-04-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-47290-9
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Article: Single-cell temporal dynamics reveals the relative contributions of transcription and degradation to cell-type specific gene expression in zebrafish embryos.

Fishman, Lior / Nechooshtan, Gal / Erhard, Florian / Regev, Aviv / Farrell, Jeffrey A / Rabani, Michal

bioRxiv : the preprint server for biology

2023

Abstract: During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the underlying regulation of mRNA transcription and degradation remains a challenge, ... ...

Abstract	During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the underlying regulation of mRNA transcription and degradation remains a challenge, especially within whole embryos with diverse cellular identities. Here, we collect temporal cellular transcriptomes of zebrafish embryos, and decompose them into their newly-transcribed (zygotic) and pre-existing (maternal) mRNA components by combining single-cell RNA-Seq and metabolic labeling. We introduce kinetic models capable of quantifying regulatory rates of mRNA transcription and degradation within individual cell types during their specification. These reveal different regulatory rates between thousands of genes, and sometimes between cell types, that shape spatio-temporal expression patterns. Transcription drives most cell-type restricted gene expression. However, selective retention of maternal transcripts helps to define the gene expression profiles of germ cells and enveloping layer cells, two of the earliest specified cell-types. Coordination between transcription and degradation restricts expression of maternal-zygotic genes to specific cell types or times, and allows the emergence of spatio-temporal patterns when overall mRNA levels are held relatively constant. Sequence-based analysis links differences in degradation to specific sequence motifs. Our study reveals mRNA transcription and degradation events that control embryonic gene expression, and provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.
Language	English
Publishing date	2023-04-21
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.20.537620
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Article ; Online: A cell-free nanobody engineering platform rapidly generates SARS-CoV-2 neutralizing nanobodies.

Chen, Xun / Gentili, Matteo / Hacohen, Nir / Regev, Aviv

Nature communications

2021 Volume 12, Issue 1, Page(s) 5506

Abstract: Antibody engineering technologies face increasing demands for speed, reliability and scale. We develop CeVICA, a cell-free nanobody engineering platform that uses ribosome display for in vitro selection of nanobodies from a library of ... ...

Abstract	Antibody engineering technologies face increasing demands for speed, reliability and scale. We develop CeVICA, a cell-free nanobody engineering platform that uses ribosome display for in vitro selection of nanobodies from a library of 10
MeSH term(s)	Antibodies, Neutralizing/immunology ; Antibodies, Viral ; COVID-19/drug therapy ; Humans ; Protein Binding ; Protein Engineering ; Reproducibility of Results ; SARS-CoV-2/drug effects ; Single-Domain Antibodies/chemistry ; Single-Domain Antibodies/genetics ; Single-Domain Antibodies/pharmacology ; Spike Glycoprotein, Coronavirus
Chemical Substances	Antibodies, Neutralizing ; Antibodies, Viral ; Single-Domain Antibodies ; Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2
Language	English
Publishing date	2021-09-17
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-25777-z
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