Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution
Cameron S. CowanMagdalena RennerMartina De GennaroBrigitte Gross-ScherfDavid GoldblumYanyan HouMartin MunzTiago M. RodriguesJacek KrólTamás SzikraRachel CuttatAnnick WaldtPanagiotis PapasaikasRoland DiggelmannClaudia P. Patino-AlvarezPatricia GallikerStefan E. SpirigDinko PavlinićNadine Gerber-HollbachSven SchuiererAldin SrdanovicMárton BaloghRiccardo PaneroÁkos KusnyerikArnold SzabóMichael StadlerSelim OrgülSimone PicelliPascal W. HaslerAndreas HierlemannHendrik P. N. SchollGuglielmo RomaFlorian NigschBotond Roska
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Abstract:
Human organoids recapitulating the cell-type diversity and function of their target organ are valuable for basic and translational research. We developed light-sensitive human retinal organoids with multiple nuclear and synaptic layers and functional synapses. We sequenced the RNA of 285,441 single cells from these organoids at seven developmental time points and from the periphery, fovea, pigment epithelium and choroid of light-responsive adult human retinas, and performed histochemistry. Cell types in organoids matured in vitro to a stable "developed" state at a rate similar to human retina development in vivo. Transcriptomes of organoid cell types converged toward the transcriptomes of adult peripheral retinal cell types. Expression of disease-associated genes was cell-type-specific in adult retina, and cell-type specificity was retained in organoids. We implicate unexpected cell types in diseases such as macular degeneration. This resource identifies cellular targets for studying disease mechanisms in organoids and for targeted repair in human retinas.Keywords:
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One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.
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Abstract Current cell–cell communication analysis focuses on quantifying intercellular interactions at cell type level. In the tissue microenvironment, one type of cells could be divided into multiple cell subgroups that function differently and communicate with other cell types or subgroups via different ligand–receptor-mediated signaling pathways. Given two cell types, we define a cell sub-crosstalk pair (CSCP) as a combination of two cell subgroups with strong and similar intercellular crosstalk signals and identify CSCPs based on coupled non-negative matrix factorization. Using single-cell spatial transcriptomics data of mouse olfactory bulb and visual cortex, we find that cells of different types within CSCPs are significantly spatially closer with each other than those in the whole single-cell spatial map. To demonstrate the utility of CSCPs, we apply 13 cell–cell communication analysis methods to sampled single-cell transcriptomics datasets at CSCP level and reveal ligand–receptor interactions masked at cell type level. Furthermore, by analyzing single-cell transcriptomics data from 29 breast cancer patients with different immunotherapy responses, we find that CSCPs are useful predictive features to discriminate patients responding to anti-PD-1 therapy from non-responders. Taken together, partitioning a cell type pair into CSCPs enables fine-grained characterization of cell–cell communication in tissue and tumor microenvironments.
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Abstract Although cell-in-cell structure was noted 100 years ago, the molecular mechanisms of ‘entering’ and the destination of cell-in-cell remain largely unclear. It takes place among the same type of cells (homotypic cell-in-cell) or different types of cells (heterotypic cell-in-cell). Cell-in-cell formation affects both effector cells and their host cells in multiple aspects, while cell-in-cell death is under more intensive investigation. Given that cell-in-cell has an important role in maintaining homeostasis, aberrant cell-in-cell process contributes to the etiopathology in humans. Indeed, cell-in-cell is observed in many pathological processes of human diseases. In this review, we intend to discuss the biological models of cell-in-cell structures under physiological and pathological status.
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Abstract Cell-free RNA (cfRNA) can be used to noninvasively measure dynamic and longitudinal physiological changes throughout the body. While there is considerable effort in the liquid biopsy field to determine disease tissue-of-origin, pathophysiology occurs at the cellular level. Here, we describe two approaches to identify cell type contributions to cfRNA. First we used Tabula Sapiens , a transcriptomic cell atlas of the human body to computationally deconvolve the cell-free transcriptome into a sum of cell type specific transcriptomes, thus revealing the spectrum of cell types readily detectable in the blood. Second, we used individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset to create cell type signature scores which can be used to infer the implicated cell types from cfRNA for a variety of diseases. Taken together, these results demonstrate that cfRNA reflects cellular contributions in health and disease from diverse cell types, potentially enabling determination of pathophysiological changes of many cell types from a single blood test.
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ABSTRACT Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,113 hereditary diseases. Overall, we identified 110 cell types affected by 714 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.
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A radioactivity-based assay was developed to define the participation of radioactively labeled cell types within the milieu of unlabeled partners in multigeneric aggregates. The cell types in these multigeneric aggregations consisted of various combinations of 21 strains representing five genera of human oral bacteria. The coaggregation properties of each cell type, when paired individually with various strains, were delineated and were unchanged when the microbes took part in the more complex multigeneric aggregations. Competition between homologous labeled and unlabeled cells for binding to a partner cell type was achieved only when the homologous cells were mixed together before the addition of their partner cells. Attempts to displace a labeled cell type from an aggregate by subsequent addition of a large excess of the same unlabeled cell type were unsuccessful, which suggested that the forces that bound different cell types together were very strong and the cell-to-cell interactions were stable. However, a cell type that exhibited only lactose-reversible coaggregations with partners was easily and selectively released by the addition of lactose to multigeneric aggregates otherwise consisting solely of lactose-nonreversible cell-to-cell interactions. This not only indicates the independent nature of individual coaggregations but also suggests the involvement of lectinlike adhesins in these sugar-inhibitable coaggregations. Although the molecular mechanisms responsible for multigeneric aggregations are unknown, the principle of a common partner cell type serving as a bridge between two otherwise noncoaggregating cell types was firmly established by the observation of sequential addition of one cell type to another. Thus, competition, bridging, coaggregate stability, independent nature of interactions, and partner specificity are the key principles of adherence that form the framework for continued studies of multigeneric aggregates. While the human oral cavity is a prime example of a complex microbial community, collectively the community appears to consist of simple and testable individual interactions.
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Summary Induced pluripotent stem cells (iPS) and direct lineage programming offer promising autologous and patient-specific sources of cells for personalized drug-testing and cell-based therapy. Before these engineered cells can be widely used, it is important to evaluate how well the engineered cell types resemble their intended target cell types. We have developed a method to generate CellScore, a cell identity score that can be used to evaluate the success of an engineered cell type in relation to both its initial and desired target cell type, which are used as references. Of 20 cell transitions tested, the most successful transitions were the iPS cells (CellScore > 0.9), while other transitions (e.g. induced hepatocytes or motor neurons) indicated incomplete transitions (CellScore < 0.5). In principle, the method can be applied to any engineered cell undergoing a cell transition, where transcription profiles are available for the reference cell types and the engineered cell type. Highlights A curated standard dataset of transcription profiles from normal cell types was created. CellScore evaluates the cell identity of engineered cell types, using the curated dataset. CellScore considers the initial and desired target cell type. CellScore identifies the most successfully engineered clones for further functional testing.
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Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.
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Diabetic peripheral neuropathy (DPN) is a common complication associated with diabetes, and can affect quality of life considerably. Dorsal root ganglion (DRG) plays an important role in the development of DPN. However, the relationship between DRG and the pathogenesis of DPN still lacks a thorough exploration. Besides, a more in-depth understanding of the cell type composition of DRG, and the roles of different cell types in mediating DPN are needed. Here we conducted single-cell RNA-seq (scRNA-seq) for DRG tissues isolated from healthy control and DPN rats. Our results demonstrated DRG includes eight cell-type populations (e.g., neurons, satellite glial cells (SGCs), Schwann cells (SCs), endothelial cells, fibroblasts). In the heterogeneity analyses of cells, six neuron sub-types, three SGC sub-types and three SC sub-types were identified, additionally, biological functions related to cell sub-types were further revealed. Cell communication analysis showed dynamic interactions between neurons, SGCs and SCs. We also found that the aberrantly expressed transcripts in sub-types of neurons, SGCs and SCs with DPN were associated with diabetic neuropathic pain, cell apoptosis, oxidative stress, etc. In conclusion, this study provides a systematic perspective of the cellular composition and interactions of DRG tissues, and suggests that neurons, SGCs and SCs play vital roles in the progression of DPN. Our data may provide a valuable resource for future studies regarding the pathophysiological effect of particular cell type in DPN.
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Every type of cell has its own special features that differentiate its members qualitatively from cells of other types. Within the same type of cells, however, every single cell also has its own unique characteristics that deviate itself from other individual cells, although they are alike collectively. These kinds of individual differences between cells are described here as cell individuality, which says basically that, within a cell population or even within a multicellular organism, every cell is a unique individual living being; and no single cell could be completely identical to another, regardless of how similar to each other they are. The individuality of a single cell can be represented by all sorts of cell characteristics, which are countless and range from physiological activities to molecular constituents. These individual differences in cell characteristics are generally presented much more in degree or in quantity, rather than in kind or in quality. Moreover, such cell individuality or quantitative variations within or even between cell populations may also play a basic role in the pathogenesis of disease, and particularly in the susceptibility of cells to the disease process.
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