Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

AbstractIdentifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined.HighlightsGeneration and evaluation of more than 130 million single-cell models with active conductances along the reconstructed morphology faithfully recapitulate the electrophysiology of 230 in vitro experiments.Optimized ion channel conductances along the cellular morphology (‘all-active’) are characteristic of model complexity and offer enhanced biophysical realism.Ion channel conductance vectors of all-active models classify transcriptomically defined cell-types.Cell type differences in ion channel conductances predicted by the models correlate with experimentally measured single-cell gene expression differences in inhibitory (Pvalb, Sst, Htr3a) and excitatory (Nr5a1, Rbp4) classes.A set of ion channel conductances identified by comparing between cell type model populations explain electrophysiology differences between these types in simulations and brain slice experiments.All-active models recapitulate multimodal properties of excitatory and inhibitory cell types offering a systematic and causal way of linking differences between them.

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CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

10.1101/2021.05.24.445360 ◽

2021 ◽

Author(s):

Zhengyu Ouyang ◽

Nathanael Bourgeois ◽

Eugenia Lyashenko ◽

Paige Cundiff ◽

Patrick F Cullen ◽

...

Keyword(s):

Single Cell ◽

Induced Pluripotent Stem Cell ◽

Cell Types ◽

Model Systems ◽

Rna Seq ◽

Cell Type ◽

Fine Grained ◽

Single Nucleus ◽

Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

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Coupling, membrane conductance, and ion channel mRNA profiles in the establishment and maintenance of network activity in the crustacean cardiac ganglion

10.32469/10355/85790 ◽

2021 ◽

Author(s):

◽

Daniel R. Kick

Keyword(s):

Gene Expression ◽

Ion Channel ◽

Gap Junction ◽

Single Cell ◽

Electrical Coupling ◽

Cell Types ◽

Network Activity ◽

Membrane Properties ◽

Cell Type ◽

Cardiac Ganglion

Neural networks produce critical rhythmic behaviors throughout an animal's lifespan, despite growth, differing environments, and changes in physiological state. This requires networks which balance stability in their properties with the plasticity necessary to respond to altered demands or perturbations. Studying the mechanisms which confer these properties requires a well characterized system with a known network topology and identifiable neurons that are amenable to both electrophysiological and molecular characterization and manipulation. Here, we use two networks from Cancer borealis to explore activity dependent regulation of cell connectivity, changes in cell properties with prolonged perturbation, and reliability of gene expression as a means for cell identification. For the first two topics we use the cardiac ganglion alone. The cardiac ganglion consists of a kernel of four interneurons that drive five motor neurons (termed large cells, LCs) which innervate the heart musculature. LCs burst synchronously due to simultaneous stimulation and electrical coupling through gap junctions. Depolarizing pharmacological perturbations have been shown to result in hyperexcitability (Ransdell et al., 2012a) and disrupt synchrony between LCs (Lane et al., 2016) eliciting rapid plasticity in ionic currents and electrical coupling which restores synchrony and excitability (Ransdell et al., 2012a; Lane et al., 2016). The salient electrophysiological signal which elicits coupling plasticity has not been identified. Using voltage clamp we directly control LC depolarizations to vary amplitude and timing of activity between LCs. We find that timing between cells, rather than depolarization elicits plasticity with the direction, i.e., potentiation or depression, being determined by the degree of desynchronization. With dynamic clamp we artificially couple networks from two animals and show that strong coupling with sufficient desynchronization can compromise a cell's output. These results suggest that coupling strength is tuned promoting synchrony or baseline cellular activity in a degree dependent manner. While rapid compensatory plasticity to hyperexcitability has been shown, it is unknown whether the changes are solely post-transcriptional and whether the short-term changes persist over longer time scales. We perturb networks for one or twenty-four hours and compare LCs' excitability, membrane properties, and abundances of ion channel and gap junction transcripts. We find evidence of rapid transcriptional changes at one hour, which may be maintained or regress at twenty-four hours. Additionally, we find that membrane properties and excitability are not maintained from one to twenty-four hours, suggesting a failure to maintain homeostasis or that additional compensatory changes are occurring at the network level. To address our third topic, we use LCs in addition to neurons collected form the stomatogastric ganglion which coordinates mastication and filtering in the digestive track. Both systems allow for unambiguous identification of cells based on anatomy or neuronal projections. We use this to evaluate the efficacy of cluster estimation procedures, clustering methods, and classification algorithms to determine the number of cell types present, group like cells together, and identify cells based on gene expression alone. We use single cell RNA-seq and single cell qRT-PCR to measure all contigs or a select set of ion channel, receptor, and gap junction mRNAs. We find these methods do not reproduce the known number of cell types present. Furthermore, although clustering and classification both outperform chance, we are unable to recapitulate cell type with complete accuracy from these data. These results indicate that, while promising, determining cell type by molecular profiling should not be relied on as the sole metric of cell type determination.

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Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells

10.1101/2020.06.21.163766 ◽

2020 ◽

Cited By ~ 1

Author(s):

Drew Neavin ◽

Quan Nguyen ◽

Maciej S. Daniszewski ◽

Helena H. Liang ◽

Han Sheng Chiu ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variation ◽

Single Cell ◽

Pluripotent Stem Cells ◽

Cell Types ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Induced Pluripotent

AbstractThe discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) - cells that can be differentiated into any cell type of the three germ layers - has provided a foundation for in vitro human disease modelling1,2, drug development1,2, and population genetics studies3,4. In the majority of instances, the expression levels of genes, plays a critical role in contributing to disease risk, or the ability to identify therapeutic targets. However, while the effect of the genetic background of cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells. Differences in the effect of genetic variation on the gene expression of different cell-types, would provide significant resolution for in vitro research using preprogramed cells. By bringing together single cell RNA sequencing15–21 and population genetics, we now have a framework in which to evaluate the cell-types specific effects of genetic variation on gene expression. Here, we performed single cell RNA-sequencing on 64,018 fibroblasts from 79 donors and we mapped expression quantitative trait loci (eQTL) at the level of individual cell types. We demonstrate that the large majority of eQTL detected in fibroblasts are specific to an individual sub-type of cells. To address if the allelic effects on gene expression are dynamic across cell reprogramming, we generated scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type specific eQTL in iPSCs, and show that that the eQTL in fibroblasts are almost entirely disappear during reprogramming. This work provides an atlas of how genetic variation influences gene expression across cell subtypes, and provided evidence for patterns of genetic architecture that lead to cell-types specific eQTL effects.

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Urine-Derived Epithelial Cells as Models for Genetic Kidney Diseases

Cells ◽

10.3390/cells10061413 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1413

Author(s):

Tjessa Bondue ◽

Fanny O. Arcolino ◽

Koenraad R. P. Veys ◽

Oyindamola C. Adebayo ◽

Elena Levtchenko ◽

...

Keyword(s):

Epithelial Cells ◽

Kidney Diseases ◽

Cell Types ◽

Cell Models ◽

Tubular Epithelial Cells ◽

Disease Mechanisms ◽

Proximal Tubular Epithelial Cells ◽

Proximal Tubular ◽

Disease Cell

Epithelial cells exfoliated in human urine can include cells anywhere from the urinary tract and kidneys; however, podocytes and proximal tubular epithelial cells (PTECs) are by far the most relevant cell types for the study of genetic kidney diseases. When maintained in vitro, they have been proven extremely valuable for discovering disease mechanisms and for the development of new therapies. Furthermore, cultured patient cells can individually represent their human sources and their specific variants for personalized medicine studies, which are recently gaining much interest. In this review, we summarize the methodology for establishing human podocyte and PTEC cell lines from urine and highlight their importance as kidney disease cell models. We explore the well-established and recent techniques of cell isolation, quantification, immortalization and characterization, and we describe their current and future applications.

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Comparative AAV-eGFP Transgene Expression Using Vector Serotypes 1–9, 7m8, and 8b in Human Pluripotent Stem Cells, RPEs, and Human and Rat Cortical Neurons

Stem Cells International ◽

10.1155/2019/7281912 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Thu T. Duong ◽

James Lim ◽

Vidyullatha Vasireddy ◽

Tyler Papp ◽

Hung Nguyen ◽

...

Keyword(s):

Cortical Neurons ◽

Transgene Expression ◽

Fluorescent Protein ◽

Ex Vivo ◽

Cell Types ◽

Egfp Expression ◽

Cell Type ◽

Egfp Gene ◽

Rat Cortical Neurons

Recombinant adeno-associated virus (rAAV), produced from a nonpathogenic parvovirus, has become an increasing popular vector for gene therapy applications in human clinical trials. However, transduction and transgene expression of rAAVs can differ acrossin vitroand ex vivo cellular transduction strategies. This study compared 11 rAAV serotypes, carrying one reporter transgene cassette containing a cytomegalovirus immediate-early enhancer (eCMV) and chicken beta actin (CBA) promoter driving the expression of an enhanced green-fluorescent protein (eGFP) gene, which was transduced into four different cell types: human iPSC, iPSC-derived RPE, iPSC-derived cortical, and dissociated embryonic day 18 rat cortical neurons. Each cell type was exposed to three multiplicity of infections (MOI: 1E4, 1E5, and 1E6 vg/cell). After 24, 48, 72, and 96 h posttransduction, GFP-expressing cells were examined and compared across dosage, time, and cell type. Retinal pigmented epithelium showed highest AAV-eGFP expression and iPSC cortical the lowest. At an MOI of 1E6 vg/cell, all serotypes show measurable levels of AAV-eGFP expression; moreover, AAV7m8 and AAV6 perform best across MOI and cell type. We conclude that serotype tropism is not only capsid dependent but also cell type plays a significant role in transgene expression dynamics.

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A novel epithelial cell from neonatal rat lung: isolation and differentiated phenotype

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.1990.259.6.l415 ◽

1990 ◽

Vol 259 (6) ◽

pp. L415-L425 ◽

Cited By ~ 10

Author(s):

P. E. Roberts ◽

D. M. Phillips ◽

J. P. Mather

Keyword(s):

Epithelial Cell ◽

Molecular Mechanisms ◽

Basal Medium ◽

Fold Increase ◽

Cell Types ◽

Cell Number ◽

Neonatal Rat ◽

Rat Lung ◽

Cell Type

A novel epithelial cell from normal neonatal rat lung has been isolated, established, and maintained for multiple passages in the absence of serum, without undergoing crisis or senescence. By careful manipulation of the nutrition/hormonal microenvironment, we have been able to select, from a heterogeneous population, a single epithelial cell type that can maintain highly differentiated features in vitro. This cell type has characteristics of bronchiolar epithelial cells. A clonal line, RL-65, has been selected and observed for greater than 2 yr in continuous culture. It has been characterized by ultrastructural, morphological, and biochemical criteria. The basal medium for this cell line is Ham's F12/Dulbecco's modified Eagle's (DME) medium plus insulin (1 micrograms/ml), human transferrin (10 micrograms/ml), ethanolamine (10(-4) M), phosphoethanolamine (10(-4) M), selenium (2.5 x 10(-8) M), hydrocortisone (2.5 x 10(-7) M), and forskolin (5 microM). The addition of 150 micrograms/ml of bovine pituitary extract to the defined basal medium stimulates a greater than 10-fold increase in cell number and a 50- to 100-fold increase in thymidine incorporation. The addition of retinoic acid results in further enhancement of cell growth and complete inhibition of keratinization. We have demonstrated a strategy that may be applicable to isolating other cell types from the lung and maintaining their differentiated characteristics for long-term culture in vitro. Such a culture system promises to be a useful model in which to study cellular events associated with differentiation and proliferation in the lung and to better understand the molecular mechanisms involved in these events.

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Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

10.1101/2020.04.12.038000 ◽

2020 ◽

Author(s):

Feng Tian ◽

Fan Zhou ◽

Xiang Li ◽

Wenping Ma ◽

Honggui Wu ◽

...

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Human Cell ◽

Expression Profiles ◽

Single Cells ◽

Cell Types ◽

List Type ◽

Cell Type ◽

Genomic Architecture ◽

Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

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Mapping single-cell atlases throughout Metazoa unravels cell type evolution

eLife ◽

10.7554/elife.66747 ◽

2021 ◽

Vol 10 ◽

Author(s):

Alexander J Tarashansky ◽

Jacob M Musser ◽

Margarita Khariton ◽

Pengyang Li ◽

Detlev Arendt ◽

...

Keyword(s):

Stem Cell ◽

Single Cell ◽

Cell Types ◽

The Self ◽

Cell Type ◽

Germ Layers ◽

Animal Evolution ◽

Self Assembling ◽

Animal Phyla ◽

Cell Data

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

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Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

Journal of Translational Medicine ◽

10.1186/s12967-021-03123-7 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Hongyu Zhao ◽

Yu Teng ◽

Wende Hao ◽

Jie Li ◽

Zhefeng Li ◽

...

Keyword(s):

Ovarian Cancer ◽

Single Cell ◽

Cancer Progression ◽

Cox Regression ◽

Single Cell Analysis ◽

Cell Types ◽

In Vitro Assays ◽

Migration And Invasion ◽

Dominant Type

Abstract Background Ovarian cancer was one of the leading causes of female deaths. Patients with OC were essentially incurable and portends a poor prognosis, presumably because of profound genetic heterogeneity limiting reproducible prognostic classifications. Methods We comprehensively analyzed an ovarian cancer single-cell RNA sequencing dataset, GSE118828, and identified nine major cell types. Relationship between the clusters was explored with CellPhoneDB. A malignant epithelial cluster was confirmed using pseudotime analysis, CNV and GSVA. Furthermore, we constructed the prediction model (i.e., RiskScore) consisted of 10 prognosis-specific genes from 2397 malignant epithelial genes using the LASSO Cox regression algorithm based on public datasets. Then, the prognostic value of Riskscore was assessed with Kaplan–Meier survival analysis and time-dependent ROC curves. At last, a series of in-vitro assays were conducted to explore the roles of IL4I1, an important gene in Riskscore, in OC progression. Results We found that macrophages possessed the most interaction pairs with other clusters, and M2-like TAMs were the dominant type of macrophages. C0 was identified as the malignant epithelial cluster. Patients with a lower RiskScore had a greater OS (log-rank P < 0.01). In training set, the AUC of RiskScore was 0.666, 0.743 and 0.809 in 1-year, 3-year and 5-year survival, respectively. This was also validated in another two cohorts. Moreover, downregulation of IL4I1 inhibited OC cells proliferation, migration and invasion. Conclusions Our work provide novel insights into our understanding of the heterogeneity among OCs, and would help elucidate the biology of OC and provide clinical guidance in prognosis for OC patients.

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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