3D computational reconstruction of tissues with hollow spherical morphologies using single-cell gene expression data

One-step biofabrication of liquid core-GelMa shell microbeads for in situ hollow cell ball self-assembly

There are many instances of hollow-structure morphogenesis in the development of tissues. Thus, the fabrication of hollow structures in a simple, high-throughput and homogeneous manner with proper natural biomaterial combination is valuable for developmental studies and tissue engineering, while it is a significant challenge in biofabrication field. We present a novel method for the fabrication of a hollow cell module using a coaxial co-flow capillary microfluidic device. Sacrificial gelatin laden with cells in the inner layer and GelMa in the outer layer are used via a coaxial co-flow capillary microfluidic device to produce homogenous micro-beads. The overall and core sizes of core-shell microbeads were well controlled. When using human vein vascular endothelial cells to demonstrate how cells line the inner surface of core-shell beads, as the core liquifies, a hollow cell ball with asymmetric features is fabricated. After release from the GelMa shell, individual cell balls are obtained and deformed cell balls can self-recover. This platform paves way for complex hollow tissue modeling in vitro, and further modulation of matrix stiffness, curvature and biochemical composition to mimic in vivo microenvironments.

Alpha cell receptor for advanced glycation end products associate with glucagon expression in type 1 diabetes

Hypoglycemia in type 1 diabetes associates with changes in the pancreatic islet α cells, where the receptor for advanced glycation end products (RAGE) is highly expressed. This study compared islet RAGE expression in donors without diabetes, those at risk of, and those with type 1 diabetes. Laser-dissected islets were subject to RNA bioinformatics and adjacent pancreatic tissue were assessed by confocal microscopy. We found that islets from type 1 diabetes donors had differential expression of the RAGE gene (AGER) and its correlated genes, based on glucagon expression. Random forest machine learning revealed that AGER was the most important predictor for islet glucagon levels. Conversely, a generalized linear model identified that glucagon expression could be predicted by expression of RAGE signaling molecules, its ligands and enzymes that create or clear RAGE ligands. Confocal imaging co-localized RAGE, its ligands and signaling molecules to the α cells. Half of the type 1 diabetes cohort comprised of adolescents and a patient with history of hypoglycemia-all showed an inverse relationship between glucagon and RAGE. These data confirm an association between glucagon and islet RAGE, its ligands and signaling pathways in type 1 diabetes, which warrants functional investigation into a role for RAGE in hypoglycemia.

Transcriptional dynamics of delaminating neuroblasts in the mouse otic vesicle

An abundance of research has recently highlighted the susceptibility of cochleovestibular ganglion (CVG) neurons to noise damage and aging in the adult cochlea, resulting in hearing deficits. Furthering our understanding of the transcriptional cascades that contribute to CVG development may provide insight into how these cells can be regenerated to treat inner ear dysfunction. Here we perform a high-depth single-cell RNA sequencing analysis of the E10.5 otic vesicle and its surrounding tissues, including CVG precursor neuroblasts and emerging CVG neurons. Clustering and trajectory analysis of otic-lineage cells reveals otic markers and the changes in gene expression that occur from neuroblast delamination toward the development of the CVG. This dataset provides a valuable resource for further identifying the mechanisms associated with CVG development from neurosensory competent cells within the otic vesicle.

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells

BACKGROUND

Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided.

RESULTS

This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio-temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio-temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate 'temporal' discriminator genes responsible for various cell type differentiations.

CONCLUSIONS

eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio-temporal formation of cellular organizations.

Deep Parallel Characterization of AAV Tropism and AAV-Mediated Transcriptional Changes via Single-Cell RNA Sequencing

Engineered variants of recombinant adeno-associated viruses (rAAVs) are being developed rapidly to meet the need for gene-therapy delivery vehicles with particular cell-type and tissue tropisms. While high-throughput AAV engineering and selection methods have generated numerous variants, subsequent tropism and response characterization have remained low throughput and lack resolution across the many relevant cell and tissue types. To fully leverage the output of these large screening paradigms across multiple targets, we have developed an experimental and computational single-cell RNA sequencing (scRNA-seq) pipeline for in vivo characterization of barcoded rAAV pools at high resolution. Using this platform, we have both corroborated previously reported viral tropisms and discovered unidentified AAV capsid targeting biases. As expected, we observed that the tropism profile of AAV.CAP-B10 in mice was shifted toward neurons and away from astrocytes when compared with AAV-PHP.eB. Transcriptomic analysis revealed that this neuronal bias is due mainly to increased targeting efficiency for glutamatergic neurons, which we confirmed by RNA fluorescence in situ hybridization. We further uncovered cell subtype tropisms of AAV variants in vascular and glial cells, such as low transduction of pericytes and Myoc+ astrocytes. Additionally, we have observed cell-type-specific transitory responses to systemic AAV-PHP.eB administration, such as upregulation of genes involved in p53 signaling in endothelial cells three days post-injection, which return to control levels by day twenty-five. The presented experimental and computational approaches for parallel characterization of AAV tropism will facilitate the advancement of safe and precise gene delivery vehicles, and showcase the power of understanding responses to gene therapies at the single-cell level.

Cell-type identity of the avian cochlea

In contrast to mammals, birds recover naturally from acquired hearing loss, which makes them an ideal model for inner ear regeneration research. Here, we present a validated single-cell RNA sequencing resource of the avian cochlea. We describe specific markers for three distinct types of sensory hair cells, including a previously unknown subgroup, which we call superior tall hair cells. We identify markers for the supporting cells associated with tall hair cells, which represent the facultative stem cells of the avian inner ear. Likewise, we present markers for supporting cells that are located below the short cochlear hair cells. We further infer spatial expression gradients of hair cell genes along the tonotopic axis of the cochlea. This resource advances neurobiology, comparative biology, and regenerative medicine by providing a basis for comparative studies with non-regenerating mammalian cochleae and for longitudinal studies of the regenerating avian cochlea.

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.

Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

Novel insights into inner ear development and regeneration for targeted hearing loss therapies

Sensorineural hearing loss is the most common sensory deficit in humans. Despite the global scale of the problem, only limited treatment options are available today. The mammalian inner ear is a highly specialized postmitotic organ, which lacks proliferative or regenerative capacity. Since the discovery of hair cell regeneration in non-mammalian species however, much attention has been placed on identifying possible strategies to reactivate similar responses in humans. The development of successful regenerative approaches for hearing loss strongly depends on a detailed understanding of the mechanisms that control human inner ear cellular specification, differentiation and function, as well as on the development of robust in vitro cellular assays, based on human inner ear cells, to study these processes and optimize therapeutic interventions. We summarize here some aspects of inner ear development and strategies to induce regeneration that have been investigated in rodents. Moreover, we discuss recent findings in human inner ear development and compare the results with findings from animal models. Finally, we provide an overview of strategies for in vitro generation of human sensory cells from pluripotent and somatic progenitors that may provide a platform for drug development and validation of therapeutic strategies in vitro.

Novel computational model of gastrula morphogenesis to identify spatial discriminator genes by self-organizing map (SOM) clustering

Deciphering the key mechanisms of morphogenesis during embryonic development is crucial to understanding the guiding principles of the body plan and promote applications in biomedical research fields. Although several computational tissue reconstruction methods using cellular gene expression data have been proposed, those methods are insufficient with regard to arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Here, we report SPRESSO, a new in silico three-dimensional (3D) tissue reconstruction method using stochastic self-organizing map (stochastic-SOM) clustering, to estimate the spatial domains of cells in tissues or organs from only their gene expression profiles. With only five gene sets defined by Gene Ontology (GO), we successfully demonstrated the reconstruction of a four-domain structure of mid-gastrula mouse embryo (E7.0) with high reproducibility (success rate = 99%). Interestingly, the five GOs contain 20 genes, most of which are related to differentiation and morphogenesis, such as activin A receptor and Wnt family member genes. Further analysis indicated that Id2 is the most influential gene contributing to the reconstruction. SPRESSO may provide novel and better insights on the mechanisms of 3D structure formation of living tissues via informative genes playing a role as spatial discriminators.

Single Cell Transcriptomics Reveal Abnormalities in Neurosensory Patterning of the Chd7 Mutant Mouse Ear

The chromatin remodeling protein CHD7 is critical for proper formation of the mammalian inner ear. Humans with heterozygous pathogenic variants in CHD7 exhibit CHARGE syndrome, characterized by hearing loss and inner ear dysplasia, including abnormalities of the semicircular canals and Mondini malformations. Chd7^Gt/+ heterozygous null mutant mice also exhibit dysplastic semicircular canals and hearing loss. Prior studies have demonstrated that reduced Chd7 dosage in the ear disrupts expression of genes involved in morphogenesis and neurogenesis, yet the relationships between these changes in gene expression and otic patterning are not well understood. Here, we sought to define roles for CHD7 in global regulation of gene expression and patterning in the developing mouse ear. Using single-cell multiplex qRT-PCR, we analyzed expression of 192 genes in FAC sorted cells from Pax2Cre;mT/mGFP wild type and Chd7^Gt/+ mutant microdissected mouse otocysts. We found that Chd7 haploinsufficient otocysts exhibit a relative enrichment of cells adopting a neuroblast (vs. otic) transcriptional identity compared with wild type. Additionally, we uncovered disruptions in pro-sensory and pro-neurogenic gene expression with Chd7 loss, including genes encoding proteins that function in Notch signaling. Our results suggest that Chd7 is required for early cell fate decisions in the developing ear that involve highly specific aspects of otic patterning and differentiation.

Growth of hollow cell spheroids in microbead templated chambers

Cells form hollow, spheroidal structures during the development of many tissues, including the ocular lens, inner ear, and many glands. Therefore, techniques for in vitro formation of hollow spheroids are valued for studying developmental and disease processes. Current in vitro methods require cells to self-organize into hollow morphologies; we explored an alternative strategy based on cell growth in predefined, spherical scaffolds. Our method uses sacrificial, gelatin microbeads to simultaneously template spherical chambers within a hydrogel and deliver cells into the chambers. We use mouse lens epithelial cells to demonstrate that cells can populate the internal surfaces of the chambers within a week to create numerous hollow spheroids. The platform supports manipulation of matrix mechanics, curvature, and biochemical composition to mimic in vivo microenvironments. It also provides a starting point for engineering organoids of tissues that develop from hollow spheroids.

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq

Conventional gene expression studies analyze multiple cells simultaneously or single cells, for which the exact in vivo or in situ position is unknown. Although cellular heterogeneity can be discerned when analyzing single cells, any spatially defined attributes that underpin the heterogeneous nature of the cells cannot be identified. Here, we describe how to use Geo-seq, a method that combines laser capture microdissection (LCM) and single-cell RNA-seq technology. The combination of these two methods enables the elucidation of cellular heterogeneity and spatial variance simultaneously. The Geo-seq protocol allows the profiling of transcriptome information from only a small number cells and retains their native spatial information. This protocol has wide potential applications to address biological and pathological questions of cellular properties such as prospective cell fates, biological function and the gene regulatory network. Geo-seq has been applied to investigate the spatial transcriptome of mouse early embryo, mouse brain, and pathological liver and sperm tissues. The entire protocol from tissue collection and microdissection to sequencing requires ∼5 d, Data analysis takes another 1 or 2 weeks, depending on the amount of data and the speed of the processor.

Single cell analysis of the inner ear sensory organs

The inner ear is composed of a complex mixture of cells, which together allow organisms to hear and maintain balance. The cells in the inner ear, which undergo an extraordinary process of development, have only recently begun to be studied on an individual level. As it has recently become clear that individual cells, previously considered to be of uniform character, may differ dramatically from each other, the need to study cell-to-cell variation, along with distinct transcriptional and regulatory signatures, has taken hold in the scientific community. In conjunction with high-throughput technologies, attempts are underway to dissect the inter- and intra-cellular variability of different cell types and developmental states of the inner ear from a novel perspective. Single cell analysis of the inner ear sensory organs holds the promise of providing a significant boost in building an omics network that translates into a comprehensive understanding of the mechanisms of hearing and balance. These networks may uncover critical elements for trans-differentiation, regeneration and/or reprogramming, providing entry points for therapeutics of deafness and vestibular pathologies.

Applications for single cell trajectory analysis in inner ear development and regeneration

Single cell trajectory analysis is a computational approach that orders cells along a pseudotime axis. This temporal modeling approach allows the characterization of transitional processes such as lineage development, response to insult, and tissue regeneration. The concept can also be applied to resolve spatial organization of cells within the originating tissue. Known as temporal and spatial transcriptomics, respectively, these methods belong to the most powerful analytical techniques for quantitative gene expression data currently available. Here, we discuss three different approaches: principal component analysis, the 'Monocle' algorithm, and self-organizing maps. We use a previously published qRT-PCR dataset of single neuroblast cells isolated from the developing mouse inner ear to highlight the basic features of the three methods and their individual limitations, as well as the distinct advantages that make them useful for research on the inner ear. The complex developmental morphogenesis of the inner ear and its specific challenges such as the paucity of cells as well as important open questions such as sensory hair cell regeneration render this organ a prime target for single cell trajectory analysis strategies.