Profiling Of Individual Cells Research Articles

The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis.

The advent of single-cell multi-omics technologies has revolutionized the landscape of preimplantation genetic diagnosis (PGD), offering unprecedented insights into the genetic, transcriptomic, and proteomic profiles of individual cells in early-stage embryos. This breakthrough holds the promise of enhancing the accuracy, efficiency, and scope of PGD, thereby significantly improving outcomes in assisted reproductive technologies (ARTs) and genetic disease prevention. This review provides a comprehensive overview of the importance of PGD in the context of precision medicine and elucidates how single-cell multi-omics technologies have transformed this field. We begin with a brief history of PGD, highlighting its evolution and application in detecting genetic disorders and facilitating ART. Subsequently, we delve into the principles, methodologies, and applications of single-cell genomics, transcriptomics, and proteomics in PGD, emphasizing their role in improving diagnostic precision and efficiency. Furthermore, we review significant recent advances within this domain, including key experimental designs, findings, and their implications for PGD practices. The advantages and limitations of these studies are analyzed to assess their potential impact on the future development of PGD technologies. Looking forward, we discuss the emerging research directions and challenges, focusing on technological advancements, new application areas, and strategies to overcome existing limitations. In conclusion, this review underscores the pivotal role of single-cell multi-omics in PGD, highlighting its potential to drive the progress of precision medicine and personalized treatment strategies, thereby marking a new era in reproductive genetics and healthcare.

ScCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained throughscResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Deciphering single-cell transcriptomic landscape of tumors in colorectal cancer treated with photothermal semiconducting polymers to design the combination therapy with checkpoint blockades for inhibition of tumor progression and metastasis

Immunotherapy is often less effective for tumors with “immune-excluded,” or “immune-desert” microenvironment. To circumvent this hurdle, new therapeutic strategies need to be designed to reprogram the tumor microenvironment (TME). It is well known that photothermal therapy (PTT) can prime the TME and provoke the systemic anti-tumor immunity. But few studies perform extensive exploration in depth to decipher the changes in TME after PTT with cutting-edge technology besides conventional flow cytometry analysis. In this study, we designed a nanosystem consisting of organic semiconductor polymer liposome nanoparticles (Y8 NPs) as a multimodal imaging and photothermal agent to image and treat tumors. Upon laser irradiation, Y8 NPs in tumors could rapidly increase the temperature, induce massive cancer cell apoptosis and also promote the immunogenic cell death (ICD), ultimately remodulating the TME. We employed single cell RNA sequencing (scRNA-seq) technology to comprehensively explore transcriptomic profiling of individual cells constituting TME, demonstrating heterogenous alterations in the subtypes of immune cells, even tumor cells. Considering the findings by scRNA-seq and TME remodulation, the combinational strategy with checkpoint blockade PD-1 antibody was designed to treat the tumors, demonstrating that the combination not only impedes primary tumor progression but also produce profound abscopal anti-tumor effect. In conclusion, we provide a simple strategy for the remodulation of TME with a photothermal nanosystem to achieve activation of anti-tumor immunity, and suppress primary and metastatic tumor progression.

Patient-Derived Organoids Recapitulate Pathological Intrinsic and Phenotypic Features of Fibrous Dysplasia.

Fibrous dysplasia (FD) is a rare bone disorder characterized by the replacement of normal bone with benign fibro-osseous tissue. Developments in our understanding of the pathophysiology and treatment options are impeded by the lack of suitable research models. In this study, we developed an in vitro organotypic model capable of recapitulating key intrinsic and phenotypic properties of FD. Initially, transcriptomic profiling of individual cells isolated from patient lesional tissues unveiled intralesional molecular and cellular heterogeneity. Leveraging these insights, we established patient-derived organoids (PDOs) using primary cells obtained from patient FD lesions. Evaluation of PDOs demonstrated preservation of fibrosis-associated constituent cell types and transcriptional signatures observed in FD lesions. Additionally, PDOs retained distinct constellations of genomic and metabolic alterations characteristic of FD. Histological evaluation further corroborated the fidelity of PDOs in recapitulating important phenotypic features of FD that underscore their pathophysiological relevance. Our findings represent meaningful progress in the field, as they open up the possibility for in vitro modeling of rare bone lesions in a three-dimensional context and may signify the first step towards creating a personalized platform for research and therapeutic studies.

Single-Cell RNA Sequencing in Organ and Cell Transplantation.

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids.

Over the past decade, single-cell transcriptomics has significantly evolved and become a standard laboratory method for simultaneous analysis of gene expression profiles of individual cells, allowing the capture of cellular diversity. In order to overcome limitations posed by difficult-to-isolate cell types, an alternative approach aiming at recovering single nuclei instead of intact cells can be utilized for sequencing, making transcriptome profiling of individual cells universally applicable. These techniques have become a cornerstone in the study of brain organoids, establishing them as models of the developing human brain. Leveraging the potential of single-cell and single-nucleus transcriptomics in brain organoid research, this protocol presents a step-by-step guide encompassing key procedures such as organoid dissociation, single-cell or nuclei isolation, library preparationand sequencing. By implementing these alternative approaches, researchers can obtain high-quality datasets, enabling the identification of neuronal and non-neuronal cell types, gene expression profiles, and cell lineage trajectories. This facilitates comprehensive investigations into cellular processes and molecular mechanisms shaping brain development.

SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication.

Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.

Abstract 194: Single-cell mechanical analysis reveals that astrocytes and glioblastoma cells have similar viscoelastic properties

Abstract Despite recent advances in treatment, the survival rate of glioblastoma (GBM) patients remains low due to the cancer’s resistance to current treatment modalities. Solid tumors like GBM are known to harbor physical abnormalities at the tissue and cell level. An understanding of GBM cell mechanics may therefore lead to the identification of novel disease mechanisms, biomarkers, and therapeutic strategies to improve treatment outcomes. In this study, we aimed to compare the mechanics of human GBM cells and their normal counterpart, immortalized human astrocytes (IHAs). To accomplish this, we used a parallel-plate flow system to expose single cells to physiological fluid shear stress and recorded the resulting deformation using a camera-coupled microscope. We then utilized digital image correlation analysis to create strain-time profiles of individual cells and fit the profiles with a three-parameter generalized Maxwell model to characterize their nuclear and cytoplasmic viscoelastic properties. We also used fluorescence microscopy and atomic force microscopy to quantify actin density and alignment and adhesion energy in both cell types, respectively. Our results showed that, contrary to extracranial cancer cells, the viscoelastic properties of GBM cells are similar to their normal counterpart. This may explain why metastasis in GBM patients is rare and conventional treatment options are unsuccessful. We also found that actin organization and localization and adhesion energy is different between GBM cells and IHAs. Thus, future research which links the genetic and microenvironmental factors that modulate these unique mechanical features may lead to the development of rational and novel clinical strategies against GBM. Citation Format: Julian Najera, Killian Onwudiwe, Luke Holen, Alice A. Burchett, Dorielis Rodriguez, Maksym Zarodniuk, Saeed Siri, Meenal Datta. Single-cell mechanical analysis reveals that astrocytes and glioblastoma cells have similar viscoelastic properties [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 194.

Single-cell transcriptomic analysis in clear cell renal cell carcinoma: Deciphering the role of APP within the tumour microenvironment.

Clear cell renal cell carcinoma (ccRCC) represents a significant challenge in oncology, primarily due to its resistance to conventional therapies. Understanding the tumour microenvironment (TME) is crucial for developing new treatment strategies. This study focuses on the role of amyloid precursor protein (APP) in tumour-associated macrophages (TAMs) within the ccRCC TME, exploring its potential as a prognostic biomarker. Basing TAM-related genes, the prognostic model was important to constructed. Employing advanced single-cell transcriptomic analysis, this research dissects the TME of ccRCC at an unprecedented cellular resolution. By isolating and examining the gene expression profiles of individual cells, particularly focusing on TAMs, the study investigates the expression levels of APP and their association with the clinical outcomes of ccRCC patients. The analysis reveals a significant correlation between the expression of APP in TAMs and patient prognosis in ccRCC. Patients with higher APP expression in TAMs showed differing clinical outcomes compared to those with lower expression. This finding suggests that APP could serve as a novel prognostic biomarker for ccRCC, providing insights into the disease progression and potential therapeutic targets. This study underscores the importance of single-cell transcriptomics in understanding the complex dynamics of the TME in ccRCC. The correlation between APP expression in TAMs and patient prognosis highlights APP as a potential prognostic biomarker. However, further research is needed to validate these findings and explore the regulatory mechanisms and therapeutic implications of APP in ccRCC.

Predicting cell types with supervised contrastive learning on cells and their types

Single-cell RNA-sequencing (scRNA-seq) is a powerful technique that provides high-resolution expression profiling of individual cells. It significantly advances our understanding of cellular diversity and function. Despite its potential, the analysis of scRNA-seq data poses considerable challenges related to multicollinearity, data imbalance, and batch effect. One of the pivotal tasks in single-cell data analysis is cell type annotation, which classifies cells into discrete types based on their gene expression profiles. In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the contrastive learning for instance-type pairs instead of instance-instance pairs. More specifically, in the cell type annotation task, the contrastive learning is applied to learn cell and cell type representation that render cells of the same type to be clustered in the new embedding space. Through this approach, the knowledge derived from annotated cells is transferred to the feature representation for scRNA-seq data. The whole training process becomes more efficient when conducting contrastive learning for cell and their types. Our experiment results demonstrate that the proposed SCLSC method consistently achieves superior accuracy in predicting cell types compared to five state-of-the-art methods. SCLSC also performs well in identifying cell types in different batch groups. The simplicity of our method allows for scalability, making it suitable for analyzing datasets with a large number of cells. In a real-world application of SCLSC to monitor the dynamics of immune cell subpopulations over time, SCLSC demonstrates a capability to discriminate cell subtypes of CD19+ B cells that were not present in the training dataset.

Dissociation of Placental Tissues for Single-Cell Techniques.

The dissociation of whole tissue into single-cell suspensions is a critical step for techniques focused on profiling of individual cells. Here, we describe a protocol for the preparation of high-quality single-cell suspensions from human placental tissues: the basal plate (BP), placental villi (PV), and chorioamniotic membranes (CAM). This protocol also provides guidance for the cryopreservation and recovery of single-cell suspensions for later use. The methods described here have been demonstrated to be suitable for downstream single-cell applications, such as single-cell RNA-sequencing, that require viable, high-quality cell suspensions.

Patch-seq: Multimodal Profiling of Single-Cell Morphology, Electrophysiology, and Gene Expression.

Cells exhibit diverse morphologic phenotypes, biophysical and functional properties, and gene expression patterns. Understanding how these features are interrelated at the level of single cells has been challenging due to the lack of techniques for multimodal profiling of individual cells. We recently developed Patch-seq, a technique that combines whole-cell patch clamp recording, immunohistochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single cells. Here we present a detailed step-by-step protocol for obtaining high-quality morphological, electrophysiological, and transcriptomic data from single cells. Patch-seq enables researchers to explore the rich, multidimensional phenotypic variability among cells and to directly correlate gene expression with phenotype at the level of single cells.

Transcriptome analysis reveals therapeutic potential of NAMPT in protecting against abdominal aortic aneurysm in human and mouse

Abdominal Aortic Aneurysm (AAA) is a life-threatening vascular disease characterized by the weakening and ballooning of the abdominal aorta, which has no effective therapeutic approaches due to unclear molecular mechanisms. Using single-cell RNA sequencing, we analyzed the molecular profile of individual cells within control and AAA abdominal aortas. We found cellular heterogeneity, with increased plasmacytoid dendritic cells and reduced endothelial cells and vascular smooth muscle cells (VSMCs) in AAA. Up-regulated genes in AAA were associated with muscle tissue development and apoptosis. Genes controlling VSMCs aberrant switch from contractile to synthetic phenotype were significantly enriched in AAA. Additionally, VSMCs in AAA exhibited cell senescence and impaired oxidative phosphorylation. Similar observations were made in a mouse model of AAA induced by Angiotensin II, further affirming the relevance of our findings to human AAA. The concurrence of gene expression changes between human and mouse highlighted the impairment of oxidative phosphorylation as a potential target for intervention. Nicotinamide phosphoribosyltransferase (NAMPT, also named VISFATIN) signaling emerged as a signature event in AAA. NAMPT was significantly downregulated in AAA. NAMPT-extracellular vesicles (EVs) derived from mesenchymal stem cells restored NAMPT levels, and offered protection against AAA. Furthermore, NAMPT-EVs not only repressed injuries, such as cell senescence and DNA damage, but also rescued impairments of oxidative phosphorylation in both mouse and human AAA models, suggesting NAMPT supplementation as a potential therapeutic approach for AAA treatment. These findings shed light on the cellular heterogeneity and injuries in AAA, and offered promising therapeutic intervention for AAA treatment.

Spatial Transcriptomics Analysis of Vulnerable Cell Populations in Middle Temporal Gyrus

AbstractBackgroundAlzheimer’s disease (AD) is characterized by cognitive decline due to histopathological changes of neurodegeneration in a stereotypical neuroanatomical progression. Applying single‐nucleus ‘omics methods has extended our understanding of how AD impacts the brain and allowed the Seattle AD Brain Cell Atlas Center (SEA‐AD) to provide a detailed cellular‐molecular view of middle temporal gyrus (MTG) in AD. Previously, we identified transcriptomically‐defined cell types that are specifically affected by AD in MTG, however, cell types identified in ‘omics data lack spatial context. Identifying the location of these cell types in situ is necessary to pinpoint the location of affected cells and connect cellular‐molecular views of disease progression to histopathology. To accomplish this, spatial transcriptomics methods measure individual mRNA molecule locations with subcellular resolution and provide transcriptomic profiles of individual cells in situ. MethodUsing a panel of genes designed to identify transcriptomic cell types, we performed spatial transcriptomics experiments using the Vizgen MERSCOPE platform across MTG tissue from 25 donors spanning AD pathology, yielding transcriptional profiles of over 1M cells across the cortical depth and white matter of 68 sections. These cells were assigned to transcriptomically‐defined cell types by integrating the spatial transcriptomics data with our existing single nucleus dataset. From the resulting maps of cell type locations, we measured cortical layer thickness, cell proportions, densities and cell type‐dependent pairwise distance distributions for each donor.ResultWe have successfully applied the MERSCOPE platform to generate cellular‐resolution spatial transcriptomic data in MTG across a wide range of AD disease progression. We applied novel deep generative models to assign cell type identity. These measurements revealed cell type‐specific spatial distributions and changes in cell type proportions due to disease, validating observations from single‐nucleus studies. Enhanced loss of Layer 2/3 excitatory neuron types and somatostatin‐expressing inhibitory neurons is calculating cell density within layers. Additional analyses uniquely available to spatial transcriptomics studies will be useful for quantitative description of cytoarchitectural changes in AD.ConclusionSpatial transcriptomics experiments across disease progression in MTG confirm cell type abundance changes measured in single‐nucleus RNASeq. The cellular spatial distributions provide detailed knowledge of the location of vulnerable cells within cortex.

Characterization of Niche Cells and Signals Regulating Stepwise Embryonic Hematopoiesis Using Slide-Seq and Merfish Spatial Transcriptomics

Stagewise HSC development initiates in the aorta-gonad-mesonephros (AGM) region during mouse embryonic day (E)9.5-E12.5 and progresses through stages including hemogenic endothelial cell (HEC) specification, the endothelial to hematopoietic transition (EHT), and maturation. The nascent HSCs (pre-HSCs) form in the AGM region and subsequently migrate to the fetal liver where they undergo further maturation and expansion. Recent advances in single cell technologies have significantly enhanced our understanding of intrinsic cellular programs involved in HSC development. However, our knowledge about niche cells and the signals they emanate in the AGM region, as well as their contribution to HSC development, remain limited. Here we've conducted spatial transcriptomics analysis using Slide-seq on mouse trunk tissues, including both the AGM region and the fetal liver, at E10.5, E11.5 and E12.5. In addition, we performed scRNA-seq on the cells collected from the AGM and trunk at E10.5, E11.5 and E12.5, as well as the fetal liver at E12.5. To integrate the Slide-seq and scRNA-seq data, we developed an algorithm that leverages capabilities from both spatial and in-depth transcriptional sequencing. As a result, we were able to successfully reconstitute the structures of the trunk tissues and obtain gene expression profiles of individual cells at 10 µm resolution. From the analysis, we identified 7 cell clusters surrounding the aorta, with one cluster comprised of Nfgr + cells predominantly occupying the dorsal aorta. The ventral part of aorta displayed a complex and multilayered cellular architecture, featuring pre-HSCs, endothelial cells, mesonephric cells, and macrophages. Notably, we identified a unique mesenchymal stromal cell (MSC) population that co-express Cdh2 (encodes N-cadherin or N-cad) and Pdgfra; thus, we call these cells N-cad + MSCs. These N-cad + MSCs were particularly enriched at the ventral part of the aorta, forming a single cell layer positioned between an inner layer of aortic endothelial cells and an outer layer of mesonephric cells. Next, we utilized several bioinformatics tools (CellphoneDB, CellChat, NicheChat, and StringDB) in effort to identify potential signals that could impact embryonic hematopoiesis. The results revealed intricate interactions between N-cad + MSCs and pre-HSCs, for example, involving JAG1-NOTCH1 and SDF-1 (encoded by Cxcl12)-CXCR4 ligand-receptor pairs. To validate the findings obtained by the Slide-seq data, we selected and designed customized RNA probes for 140 genes of interest, including cell markers and potential signals. We then conducted MERFISH (Multiplexed Error-Robust Fluorescence in situ Hybridization) on 18 E11.5 trunk sections. The MERFISH data enabled us to characterize the spatially resolved niche cells and their derived signals in the AGM region at the single-cell level, which largely corroborated the results obtained from the Slide-seq data. Specifically, the MERFISH data indicates that N-cad + MSCs are in direct contact with aortic endothelial cells and are the primary cellular source of Jag1 and Cxcl12 around the aorta. Given the unique position of N-cad + MSCs and their intricate interactions with pre-HSCs, we formulated the hypothesis that N-cad + MSCs play a crucial role in embryonic hematopoiesis. To functionally validate our hypothesis, we are conducting a series of experiments using Ncad CreER; Cxcl12 flox/floxand Ncad CreER; Jag1 flox/flox mouse models. We anticipate that embryos lacking N-cad + MSC derived Jag1 or Cxcl12 will exhibit a substantial decline in intra-aortic hematopoietic clusters, phenotypic pre-HSCs, and functional HSCs. In summary, our study employed sequencing-based (Slide-seq) and imaging-based (MERFISH) spatial transcriptomics technologies to successfully characterize dynamic, spatially resolved profiles of niche components and associated signaling in the AGM region at the single cell level. This work significantly enhances our understanding of niche signaling and its role in regulating embryonic hematopoiesis and may aid the development of novel methods for inducing the transition from pluripotent stem cells to HSCs.

Spatial transcriptomics in neuroscience.

The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.

GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL.

Food nutrition and toxicology targeting on specific organs in the era of single-cell sequencing

Due to the complex natures of dietary food components, it is difficult to elucidate how the compounds affect host health. Dietary food often selectively presents its mechanism of action on different cell types, and participates in the modulation of targeted cells and their microenvironments within organs. However, the limitations of traditional in vitro assays or in vivo animal experiments cannot comprehensively examine cellular heterogeneity and the tissue-biased influences. Single-cell RNA sequencing (scRNA-seq) has emerged as an indispensable methodology to decompose tissues into different cell types for the demonstration of transcriptional profiles of individual cells. ScRNA-seq applications has been summarized on three typical organs (brain, liver, kidney), and two representative immune- and tumor related health problems. The ever-increasing role of scRNA-seq in dietary food research with further improvement can provide sub-cellular information and the coupling between other cellular modalities. In this review, we propose utilizing scRNA-seq to more effectively capture the subtle and complex effects of food chemicals, and how they may lead to health problems at single-cell resolution. This novel technique will be valuable to elucidate the underlying mechanism of both the health benefits of food nutrients and the detrimental consequences food toxicants at the cellular level.

A Non-canonical Excitatory PV RGC-PV SC Visual Pathway for Mediating the Looming-evoked Innate Defensive Response.

Parvalbumin-positive retinal ganglion cells (PV+ RGCs) are an essential subset of RGCs found in various species. However, their role in transmitting visual information remains unclear. Here, we characterized PV+ RGCs in the retina and explored the functions of the PV+ RGC-mediated visual pathway. By applying multiple viral tracing strategies, we investigated the downstream of PV+ RGCs across the whole brain. Interestingly, we found that the PV+ RGCs provided direct monosynaptic input to PV+ excitatory neurons in the superficial layers of the superior colliculus (SC). Ablation or suppression of SC-projecting PV+ RGCs abolished or severely impaired the flight response to looming visual stimuli in mice without affecting visual acuity. Furthermore, using transcriptome expression profiling of individual cells and immunofluorescence colocalization for RGCs, we found that PV+ RGCs are predominant glutamatergic neurons. Thus, our findings indicate the critical role of PV+ RGCs in an innate defensive response and suggest a non-canonical subcortical visual pathway from excitatory PV+ RGCs to PV+ SC neurons that regulates looming visual stimuli. These results provide a potential target for intervening and treating diseases related to this circuit, such as schizophrenia and autism.