Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Eraslan, Gökçen; Drokhlyansky, Eugene; Anand, Shankara; Fiškin, Evgenij; Subramanian, Ayshwarya; Slyper, Michal; Wang, Jiali; Wittenberghe, Nicholas Van; Rouhana, John M.; Waldman, Julia; Ashenberg, Orr; Lek, Monkol; Dionne, Danielle; Win, Thet Su; Cuoco, Michael S.; Kuksenko, Olena; Tsankov, Alexander M.; Branton, Philip A.; Marshall, Jamie L.; Greka, Anna; Getz, Gad; Segrè, Ayellet V.; Aguet, François; Rozenblatt-Rosen, Orit; Ardlie, Kristin; Regev, Aviv

doi:10.1126/science.abl4290

Cited by 213 publications

(241 citation statements)

References 200 publications

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“…There is a long history of constructing transcriptomic and protein expression atlases to tackle outstanding questions in morphogenesis [1][2][3][4][5][6][7]. Existing atlases have been, by necessity, static representations of embryo components at a collection of specific points in time [4,8,9].…”

Section: Introductionmentioning

confidence: 99%

Morphodynamic Atlas for Drosophila Development

Mitchell

Lefebvre

Jain-Sharma

et al. 2022

Preprint

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During morphogenesis, diverse cell scale and tissue scale processes couple to dynamically sculpt organs. In this coupling, genetic expression patterns and biochemical signals regulate and respond to mechanical deformations to ensure reproducible and robust changes in tissue geometry. A long standing approach to characterize these interactions has been the construction of expression atlases, and these atlases have necessarily relied on fixed snapshots of embryogenesis. Addressing how expression profiles relate to tissue dynamics, however, requires a scheme for spatiotemporal registration across different classes of data that incorporates both live samples and fixed datasets. Here, we construct a morphodynamic atlas that unifies fixed and live datasets, from gene expression profiles to cytoskeletal components, into a single, morphological consensus timeline. This resource and our computational approach to global alignment facilitate hypothesis testing using quantitative comparison of data both within and across ensembles, with resolution in both space and time to relate genes to tissue rearrangement, cell behaviors, and out-of-plane motion. Examination of embryo kinematics reveals stages in which tissue flow patterns are quasi-stationary, arranged as a sequence of morphodynamic modules. Temperature perturbations tune the duration of one such module, during body axis elongation, according to a simple, parameter-free scaling in which the total integrated tissue deformation is achieved at a temperature dependent rate. By extending our approach to visceral organ formation during later stages of embryogenesis, we highlight how morphodynamic atlases can incorporate complex shapes deforming in 3D. In this context, morphodynamic modules are reflected in some, but not all, measures of tissue motion. Our approach and the resulting atlas opens up the ability to quantitatively test hypotheses with resolution in both space and time, relating genes to tissue rearrangement, cell behaviors, and organ motion.

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Section: Introductionmentioning

confidence: 99%

Morphodynamic Atlas for Drosophila Development

Mitchell

Lefebvre

Jain-Sharma

et al. 2022

Preprint

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“…Atlases constructed from even a modest number of samples, such as outlined here, represent critical resources for discovery efforts and translational research (Börner et al, 2021; Conde et al, 2022; Eraslan et al, 2022; Jones et al, 2022; Liu and Zhang, 2022; Regev et al, 2017; Rozenblatt-Rosen et al, 2020; Snyder et al, 2019). Accordingly, our multi-scale, multimodal approach yielded several insights and technical advances.…”

Section: Discussionmentioning

confidence: 99%

“…A chief aim of several international consortia is the construction of organ atlases from healthy and diseased human populations (Liu and Zhang, 2022; Regev et al, 2017; Rozenblatt-Rosen et al, 2020; Snyder et al, 2019). These efforts are focused on generating atlases from human organs and tissues using next generation sequencing (NGS) and spatial approaches such as multiplexed antibody-based imaging (Börner et al, 2021; Conde et al, 2022; Eraslan et al, 2022; Hickey et al, 2021; Jones et al, 2022; Suo et al, 2022). The lymph node (LN) offers unique challenges and opportunities due to its importance in host immune responses, structural and cellular diversity, distribution in >500 discrete units throughout the human body, and involvement in metastasis and hematological malignancies (Grant et al, 2020; Swerdlow et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes

Radtke

Postovalova

Varlamova

et al. 2022

Preprint

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Reference atlases, molecular and spatial maps of mammalian tissues, are critical resources for discovery efforts and translational research. Their utility is dependent on operationalizing the resulting data by identifying cell types, histological patterns, and predictive biomarkers underlying health and disease. The human lymph node (LN) offers a compelling use case because of its importance in immunity, structural and cellular diversity, and neoplastic involvement. One hematological malignancy, follicular lymphoma (FL), evolves from developmentally blocked germinal center B cells residing in and trafficking through these tissues. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Here, we present an integrated portrait of healthy and FL LNs using multiple genomic and advanced imaging technologies. By leveraging the strengths of each platform, we identified several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk of FL patients.

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“…The first step for a targeted strategy as proposed here is accurate phenotyping of the patient's symptoms and/or congenital malformations. Depending on the suspected disease, the search for the causative variant(s) will focus on a specific tissue or cell type [18]. Highly tissue-specific manifestations of genetic diseases are due to the deregulation of a functional subnetwork of genes (disease module), rather than a single gene [19].…”

Section: Discussionmentioning

confidence: 99%

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

Fernàndez

Yubero

Palau

et al. 2022

IJMS

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There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein’s function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein’s loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.

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Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Cited by 213 publications

References 200 publications

Morphodynamic Atlas for Drosophila Development

Morphodynamic Atlas for Drosophila Development

A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

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