Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization.
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ABSTRACT:
SUBMITTER: Lee BR
PROVIDER: S-EPMC8428855 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The mammalian neocortex is a layered sheet of neural tissue that mediates complex cognitive processes including perception and cognition. Despite its importance, we still lack detailed knowledge of the cellular components of the neocortex. Integrating morphological, electrophysiological and molecular classification schemes into a common framework for defining cell types is challenging due to the limited scope of most single-cell analyses. Here, we developed a protocol for high-throughput electrophysiological and transcriptomic analysis of single neurons that combines whole-cell recordings and single-cell RNA-sequencing, which we call Patch-seq. Using this approach, we fully characterized the electrophysiological and molecular profiles of ~50 neocortical neurons, and show that gene expression patterns can be used to infer the morphological and physiological properties of individual neurons and their corresponding cell type. Our results shed light on the molecular underpinnings of neuronal diversity and demonstrate a path forward for comprehensive cell type characterization in the nervous system.
2016-01-25 | E-MTAB-4092 | biostudies-arrayexpress
Project description:In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were "class-driven," meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from PatchSeq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.
| S-EPMC6599125 | biostudies-literature
Project description:Brain organoids represent a new model system for studying developmental human neurophysiology. Methods for studying the electrophysiology and morphology of single neurons in organoids require acute slices or dissociated cultures. While these methods have advantages (e.g., visual access, ease of experimentation), they risk damaging cells and circuits present in the intact organoid. To access single cells within intact organoid circuits, we have demonstrated a method for fixturing and performing whole cell patch clamp recording from intact brain organoids using both manual and automated tools. We demonstrate applied electrophysiology methods development followed by an integration of electrophysiology with reconstructing the morphology of the neurons within the brain organoid using dye filling and tissue clearing. We found that whole cell patch clamp recordings could be achieved both on the surface and within the interior of intact human brain organoids using both manual and automated methods. Manual experiments were higher yield (53 % whole cell success rate manual, 9 % whole cell success rate automated), but automated experiments were more efficient (30 patch attempts per day automated, 10 patch attempts per day manual). Using these methods, we performed an unbiased survey of cells within human brain organoids between 90 and 120 days in vitro (DIV) and present preliminary data on morphological and electrical diversity in human brain organoids. The further development of intact brain organoid patch clamp methods could be broadly applicable to studies of cellular, synaptic, and circuit-level function in the developing human brain.
| S-EPMC10483933 | biostudies-literature
Project description:The classical sacrococcygeal chordoma tumor presents with a typical morphology of lobulated myxoid tumor tissue with cords, strands and nests of tumor cells consisting of small non-vacuolated cells, intermediate cells with a wide range of vacuolization and large heavily vacuolated (physaliferous) cells. Because of its rare incidence, lack of suited model systems and technical limitations analysis was only performed on bulk tumor mass neglecting its heterogeneous composition. We aimed at elucidating the differences between small non-vacuolated and large physaliferous cells on the genomic and transcriptomic level. Secondly, we intended to clarify whether the observed cell types are derived from genetically distinct clones or rather represent different phenotypes. Using the chordoma cell line MUG-Chor1 we monitored morphological changes via time lapse experiments. We isolated pure fractions of each phenotype by means of laser microdissection or micromanipulation allowing phenotype-specific analysis. Pools of 100 cells each were genetically profiled after whole genome amplification by array comparative genomic hybridization. For expression analysis 20 cells each were subjected to whole transcriptom amplification, forwarded to RNA microarray analysis and qRT-PCR. Time lapse analysis unveiled small non-vacuolated cells to develop into large physaliferous cells via intermediate cells containing an increasing amount of vacuoles. Furthermore, we showed small and large physaliferous cells to proliferate at the same rate but intermediate cells to be the most proliferating cell phenotype. Small non-vacuolated and large physaliferous cells showed identical copy number variations. Despite their obvious morphological disparities we detected only modest changes in over all gene expression. However, verification of candidate genes yielded significant up-regulation of ALG11 (700-fold), PPP2CB (18.6-fold), and UCHL3 (18.7-fold) in large physaliferous cells. Of two different cell types (large and small MUG-Chor1 cells) we analysed each in triplicates. In total 6 cell pools were analysed.
2013-08-30 | E-GEOD-48779 | biostudies-arrayexpress
Project description:How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.
| S-EPMC5673240 | biostudies-literature
Project description:Recent advances in the field of cellular reprogramming have opened a route to studying the fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single-neuron to subcellular features. In this work, HD-MEAs are used in vitro to characterize and compare human induced-pluripotent-stem-cell-derived dopaminergic and motor neurons, including isogenic neuronal lines modeling Parkinson's disease and amyotrophic lateral sclerosis. Reproducible electrophysiological network, single-cell and subcellular metrics are used for phenotype characterization and drug testing. Metrics, such as burst shape and axonal velocity, enable the distinction of healthy and diseased neurons. The HD-MEA metrics can also be used to detect the effects of dosing the drug retigabine to human motor neurons. Finally, it is shown that the ability to detect drug effects and the observed culture-to-culture variability critically depend on the number of available recording electrodes.
| S-EPMC7610355 | biostudies-literature
Project description:The classical sacrococcygeal chordoma tumor presents with a typical morphology of lobulated myxoid tumor tissue with cords, strands and nests of tumor cells consisting of small non-vacuolated cells, intermediate cells with a wide range of vacuolization and large heavily vacuolated (physaliferous) cells. Because of its rare incidence, lack of suited model systems and technical limitations analysis was only performed on bulk tumor mass neglecting its heterogeneous composition. We aimed at elucidating the differences between small non-vacuolated and large physaliferous cells on the genomic and transcriptomic level. Secondly, we intended to clarify whether the observed cell types are derived from genetically distinct clones or rather represent different phenotypes. Using the chordoma cell line MUG-Chor1 we monitored morphological changes via time lapse experiments. We isolated pure fractions of each phenotype by means of laser microdissection or micromanipulation allowing phenotype-specific analysis. Pools of 100 cells each were genetically profiled after whole genome amplification by array comparative genomic hybridization. For expression analysis 20 cells each were subjected to whole transcriptom amplification, forwarded to RNA microarray analysis and qRT-PCR. Time lapse analysis unveiled small non-vacuolated cells to develop into large physaliferous cells via intermediate cells containing an increasing amount of vacuoles. Furthermore, we showed small and large physaliferous cells to proliferate at the same rate but intermediate cells to be the most proliferating cell phenotype. Small non-vacuolated and large physaliferous cells showed identical copy number variations. Despite their obvious morphological disparities we detected only modest changes in over all gene expression. However, verification of candidate genes yielded significant up-regulation of ALG11 (700-fold), PPP2CB (18.6-fold), and UCHL3 (18.7-fold) in large physaliferous cells.
2013-08-30 | GSE48779 | GEO
Project description:Morphological, electrophysiological and transcriptomic profiling of single neurons using Patch-seq
| PRJEB11960 | ENA
Project description:Cerebral organoids (COs) are rapidly accelerating the rate of translational neuroscience based on their potential to model complex features of the developing human brain. Several studies have examined the electrophysiological and neural network features of COs; however, no study has comprehensively investigated the developmental trajectory of electrophysiological properties in whole-brain COs and correlated these properties with developmentally-linked morphological and cellular features. Here, we profiled the neuroelectrical activities of COs over the span of five months with a multi-electrode array (MEA) platform and observed the emergence and maturation of several electrophysiologic properties, including rapid firing rates and network bursting events. To complement these analyses, we characterized the complex molecular and cellular development that give rise to these mature neuroelectrical properties with immunohistochemical and single-cell transcriptomic analyses. This integrated approach underscores the value of COs as an emerging model system of human brain development and neurological disease.
2020-08-28 | GSE157019 | GEO
Project description:Background: Endometrioid carcinoma (EC) and clear cell carcinoma (CC) histotypes of epithelial ovarian cancer are understudied compared with the more common high-grade serous carcinomas (HGSC). We therefore sought to characterize EC and CC transcriptomes in relation to HGSC.Methods: Following bioinformatics processing and gene abundance normalization, differential expression analysis of RNA sequence data collected on fresh-frozen tumors was completed with nonparametric statistical analysis methods (55 ECs, 19 CCs, 112 HGSCs). Association of gene expression with progression-free survival (PFS) was completed with Cox proportional hazards models. Eight additional multi-histotype expression array datasets (N = 852 patients) were used for replication.Results: In the discovery set, tumors generally clustered together by histotype. Thirty-two protein-coding genes were differentially expressed across histotype (P < 1 × 10-10) and showed similar associations in replication datasets, including MAP2K6, KIAA1324, CDH1, ENTPD5, LAMB1, and DRAM1 Nine genes associated with PFS (P < 0.0001) showed similar associations in replication datasets. In particular, we observed shorter PFS time for CC and EC patients with high gene expression for CCNB2, CORO2A, CSNK1G1, FRMD8, LIN54, LINC00664, PDK1, and PEX6, whereas, the converse was observed for HGSC patients.Conclusions: The results suggest important histotype differences that may aid in the development of treatment options, particularly those for patients with EC or CC.Impact: We present replicated findings on transcriptomic differences and how they relate to clinical outcome for two of the rarer ovarian cancer histotypes of EC and CC, along with comparison with the common histotype of HGSC. Cancer Epidemiol Biomarkers Prev; 27(9); 1101-9. ©2018 AACR.
| S-EPMC6719542 | biostudies-literature