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Robust RNA-Seq of aRNA-amplified single cell material collected by patch clamp.

ABSTRACT: Most single cell RNA sequencing protocols start with single cells dispersed from intact tissue. High-throughput processing of the separated cells is enabled using microfluidics platforms. However, dissociation of tissue results in loss of information about cell location and morphology and potentially alters the transcriptome. An alternative approach for collecting RNA from single cells is to re-purpose the electrophysiological technique of patch clamp recording. A hollow patch pipette is attached to individual cells, enabling the recording of electrical activity, after which the cytoplasm may be extracted for single cell RNA-Seq ("Patch-Seq"). Since the tissue is not disaggregated, the location of cells is readily determined, and the morphology of the cells is maintained, making possible the correlation of single cell transcriptomes with cell location, morphology and electrophysiology. Recent Patch-Seq studies utilizes PCR amplification to increase amount of nucleic acid material to the level required for current sequencing technologies. PCR is prone to create biased libraries - especially with the extremely high degrees of exponential amplification required for single cell amounts of RNA. We compared a PCR-based approach with linear amplifications and demonstrate that aRNA amplification (in vitro transcription, IVT) is more sensitive and robust for single cell RNA collected by a patch clamp pipette.

SUBMITTER: Kim JMH

PROVIDER: S-EPMC7004989 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Robust RNA-Seq of aRNA-amplified single cell material collected by patch clamp

Project description:Most single cell RNA sequencing protocols start with single cells dispersed from intact tissue. High-throughput processing of the separated cells is enabled using microfluidics platforms. However, dissociation of tissue results in loss of information about cell location and morphology and potentially alters the transcriptome. An alternative approach for collecting RNA from single cells is to re-purpose the electrophysiological technique of patch clamp recording. A hollow patch pipette is attached to individual cells, enabling the recording of electrical activity, after which the cytoplasm may be extracted for single cell RNA-Seq (“Patch-Seq”). Since the tissue is not disaggregated, the location of cells is readily determined, and the morphology of the cells is maintained, making possible the correlation of single cell transcriptomes with cell location, morphology and electrophysiology. Recent Patch-Seq studies utilizes PCR amplification to increase amount of nucleic acid material to the level required for current sequencing technologies. PCR is prone to create biased libraries – especially with the extremely high degrees of exponential amplification required for single cell amounts of RNA. We compared a PCR-based approach with linear amplifications and demonstrate that aRNA amplification (in vitro transcription, IVT) is more sensitive and robust for single cell RNA collected by a patch clamp pipette.

2020-01-25 | GSE144216 | GEO

Robust RNA-Seq of aRNA-amplified single cell material collected by patch clamp

Project description:Robust RNA-Seq of aRNA-amplified single cell material collected by patch clamp

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Patch clamp-assisted single neuron lipidomics.

Project description:Our understanding of the physiological and pathological functions of brain lipids is limited by the inability to analyze these molecules at cellular resolution. Here, we present a method that enables the detection of lipids in identified single neurons from live mammalian brains. Neuronal cell bodies are captured from perfused mouse brain slices by patch clamping, and lipids are analyzed using an optimized nanoflow liquid chromatography/mass spectrometry protocol. In a first application of the method, we identified more than 40 lipid species from dentate gyrus granule cells and CA1 pyramidal neurons of the hippocampus. This survey revealed substantial lipid profile differences between neurons and whole brain tissue, as well as between resting and physiologically stimulated neurons. The results suggest that patch clamp-assisted single neuron lipidomics could be broadly applied to investigate neuronal lipid homeostasis in healthy and diseased brains.

| S-EPMC5509708 | biostudies-literature

Correlating Anatomy and Function with Gene Expression in Individual Neurons by Combining in Vivo Labeling, Patch Clamp, and Single Cell RNA-seq.

Project description:The classification of neurons into distinct types is an ongoing effort aimed at revealing and understanding the diversity of the components of the nervous system. Recently available methods allow us to determine the gene expression pattern of individual neurons in the mammalian cerebral cortex to generate powerful categorization schemes. For a thorough understanding of neuronal diversity such genetic categorization schemes need to be combined with traditional classification parameters like position, axonal projection or response properties to sensory stimulation. Here we describe a method to link the gene expression of individual neurons with their position, axonal projection, or sensory response properties. Neurons are labeled in vivo based on their anatomical or functional properties and, using patch clamp pipettes, their RNA individually harvested in vitro for RNAseq. We validate the methodology using multiple established molecularly and anatomically distinct cell populations and explore molecular differences between uncharacterized neurons in mouse visual cortex. Gene expression patterns between L5 neurons projecting to frontal or contralateral cortex are distinct while L2 neurons differing in position, projection, or function are molecularly similar. With this method we can determine the genetic expression pattern of functionally and anatomically identified individual neurons.

| S-EPMC5714881 | biostudies-literature

SCnorm: robust normalization of single-cell RNA-seq data.

Project description:The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.

| S-EPMC5473255 | biostudies-literature

Clinical interpretation of KCNH2 variants using a robust PS3/BS3 functional patch-clamp assay.

Project description:Long QT syndrome (LQTS), caused by the dysfunction of cardiac ion channels, increases the risk of sudden death in otherwise healthy young people. For many variants in LQTS genes, there is insufficient evidence to make a definitive genetic diagnosis. We have established a robust functional patch-clamp assay to facilitate classification of missense variants in KCNH2, one of the key LQTS genes. A curated set of 30 benign and 30 pathogenic missense variants were used to establish the range of normal and abnormal function. The extent to which variants reduced protein function was quantified using Z scores, the number of standard deviations from the mean of the normalized current density of the set of benign variant controls. A Z score of -2 defined the threshold for abnormal loss of function, which corresponds to 55% wild-type function. More extreme Z scores were observed for variants with a greater loss-of-function effect. We propose that the Z score for each variant can be used to inform the application and weighting of abnormal and normal functional evidence criteria (PS3 and BS3) within the American College of Medical Genetics and Genomics variant classification framework. The validity of this approach was demonstrated using a series of 18 KCNH2 missense variants detected in a childhood onset LQTS cohort, where the level of function assessed using our assay correlated to the Schwartz score (a scoring system used to quantify the probability of a clinical diagnosis of LQTS) and the length of the corrected QT (QTc) interval.

| S-EPMC10840334 | biostudies-literature

Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq.

Project description:Most nematodes are small worms that lack enough RNA for regular RNA-seq protocols without pooling hundred to thousand of individuals. We have adapted the Smart-seq2 protocol in order to sequence the transcriptome of an individual worm. While developed for individual Steinernema carpocapsae and Caenorhabditis elegans larvae as well as embryos, the protocol should be adaptable for other nematode species and small invertebrates. In addition, we describe how to analyze the RNA-seq results using the Galaxy online environment. We expect that this method will be useful for the studying gene expression variances of individual nematodes in wild type and mutant backgrounds.

| S-EPMC5857950 | biostudies-literature

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp.

Project description:The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers easy identification and characterization of these cell types, prerequisites to study their specific functions. This article describes the multiplex single cell reverse transcription polymerase chain reaction (RT-PCR) protocol, which allows, after patch-clamp recording in slices, to detect simultaneously the expression of tens of genes in a single cell. This simple method can be implemented with morphological characterization and is widely applicable to determine the phenotypic traits of various cell types and their particular cellular environment, such as in the vicinity of blood vessels. The principle of this protocol is to record a cell with the patch-clamp technique, to harvest and reverse transcribe its cytoplasmic content, and to detect qualitatively the expression of a predefined set of genes by multiplex PCR. It requires a careful design of PCR primers and intracellular patch-clamp solution compatible with RT-PCR. To ensure a selective and reliable transcript detection, this technique also requires appropriate controls from cytoplasm harvesting to amplification steps. Although precautions discussed here must be strictly followed, virtually any electrophysiological laboratory can use the multiplex single cell RT-PCR technique.

| S-EPMC6101963 | biostudies-literature

A robust semi-supervised NMF model for single cell RNA-seq data.

Project description:BackgroundSingle-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand cell function and constitutes the basis of other advanced analysis. Nonnegative Matrix Factorization (NMF) has been widely used in clustering analysis of transcriptome data and achieved good performance. However, the existing NMF model is unsupervised and ignores known gene functions in the process of clustering. Knowledges of cell markers genes (genes that only express in specific cells) in human and model organisms have been accumulated a lot, such as the Molecular Signatures Database (MSigDB), which can be used as prior information in the clustering analysis of scRNA-seq data. Because the same kind of cells is likely to have similar biological functions and specific gene expression patterns, the marker genes of cells can be utilized as prior knowledge in the clustering analysis.MethodsWe propose a robust and semi-supervised NMF (rssNMF) model, which introduces a new variable to absorb noises of data and incorporates marker genes as prior information into a graph regularization term. We use rssNMF to solve the clustering problem of scRNA-seq data.ResultsTwelve scRNA-seq datasets with true labels are used to test the model performance and the results illustrate that our model outperforms original NMF and other common methods such as KMeans and Hierarchical Clustering. Biological significance analysis shows that rssNMF can identify key subclasses and latent biological processes. To our knowledge, this study is the first method that incorporates prior knowledge into the clustering analysis of scRNA-seq data.

| S-EPMC7571410 | biostudies-literature

A robust nonlinear low-dimensional manifold for single cell RNA-seq data.

Project description:BackgroundModern developments in single-cell sequencing technologies enable broad insights into cellular state. Single-cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden our understanding of cellular heterogeneity in tissues and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single-cell data. However, methods have yet to be developed for unfiltered and unnormalized count data that estimate uncertainty in the low-dimensional space. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data.ResultsGene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student's t-distribution to estimate a manifold that is robust to technical and biological noise found in normalized scRNA-seq data. We compare our approach to common dimension reduction tools across a diverse set of scRNA-seq data sets to highlight our model's ability to enable important downstream tasks such as clustering, inferring cell developmental trajectories, and visualizing high throughput experiments on available experimental data.ConclusionWe show that our adaptive robust statistical approach to estimate a nonlinear manifold is well suited for raw, unfiltered gene counts from high-throughput sequencing technologies for visualization, exploration, and uncertainty estimation of cell states.

| S-EPMC7374962 | biostudies-literature

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