Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity

Single-Cell RNA Sequencing and Its Applications in Pituitary Research

BACKGROUND

Mounting evidence underscores the significance of cellular diversity within the endocrine system and the intricate interplay between different cell types and tissues, essential for preserving physiological balance and influencing disease trajectories. The pituitary gland, a central player in the endocrine orchestra, exemplifies this complexity with its assortment of hormone-secreting and nonsecreting cells.

SUMMARY

The pituitary gland houses several types of cells responsible for hormone production, alongside nonsecretory cells like fibroblasts and endothelial cells, each playing a crucial role in the gland's function and regulatory mechanisms. Despite the acknowledged importance of these cellular interactions, the detailed mechanisms by which they contribute to pituitary gland physiology and pathology remain largely uncharted. The last decade has seen the emergence of groundbreaking technologies such as single-cell RNA sequencing, offering unprecedented insights into cellular heterogeneity and interactions. However, the application of this advanced tool in exploring the pituitary gland's complexities has been scant. This review provides an overview of this methodology, highlighting its strengths and limitations, and discusses future possibilities for employing it to deepen our understanding of the pituitary gland and its dysfunction in disease states.

KEY MESSAGE

Single-cell RNA sequencing technology offers an unprecedented means to study the heterogeneity and interactions of pituitary cells, though its application has been limited thus far. Further utilization of this tool will help uncover the complex physiological and pathological mechanisms of the pituitary, advancing research and treatment of pituitary diseases.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Applications of single-cell RNA sequencing in atopic dermatitis and psoriasis

Single-cell RNA sequencing (scRNA-seq) is a novel technology that characterizes molecular heterogeneity at the single-cell level. With the development of more automated, sensitive, and cost-effective single-cell isolation methods, the sensitivity and efficiency of scRNA-seq have improved. Technological advances in single-cell analysis provide a deeper understanding of the biological diversity of cells present in tissues, including inflamed skin. New subsets of cells have been discovered among common inflammatory skin diseases, such as atopic dermatitis (AD) and psoriasis. ScRNA-seq technology has also been used to analyze immune cell distribution and cell-cell communication, shedding new light on the complex interplay of components involved in disease responses. Moreover, scRNA-seq may be a promising tool in precision medicine because of its ability to define cell subsets with potential treatment targets and to characterize cell-specific responses to drugs or other stimuli. In this review, we briefly summarize the progress in the development of scRNA-seq technologies and discuss the latest scRNA-seq-related findings and future trends in AD and psoriasis. We also discuss the limitations and technical problems associated with current scRNA-seq technology.

Differential vulnerability of anterior cingulate cortex cell types to diseases and drugs

In psychiatric disorders, mismatches between disease states and therapeutic strategies are highly pronounced, largely because of unanswered questions regarding specific vulnerabilities of different cell types and therapeutic responses. Which cellular events (housekeeping or salient) are most affected? Which cell types succumb first to challenges, and which exhibit the strongest response to drugs? Are these events coordinated between cell types? How does disease and drug effect this coordination? To address these questions, we analyzed single-nucleus-RNAseq (sn-RNAseq) data from the human anterior cingulate cortex-a region involved in many psychiatric disorders. Density index, a metric for quantifying similarities and dissimilarities across functional profiles, was employed to identify common or salient functional themes across cell types. Cell-specific signatures were integrated with existing disease and drug-specific signatures to determine cell-type-specific vulnerabilities, druggabilities, and responsiveness. Clustering of functional profiles revealed cell types jointly participating in these events. SST and VIP interneurons were found to be most vulnerable, whereas pyramidal neurons were least. Overall, the disease state is superficial layer-centric, influences cell-specific salient themes, strongly impacts disinhibitory neurons, and influences astrocyte interaction with a subset of deep-layer pyramidal neurons. In absence of disease, drugs profiles largely recapitulate disease profiles, offering a possible explanation for drug side effects. However, in presence of disease, drug activities, are deep layer-centric and involve activating a distinct subset of deep-layer pyramidal neurons to circumvent the disease state's disinhibitory circuit malfunction. These findings demonstrate a novel application of sn-RNAseq data to explain drug and disease action at a systems level.

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

NEIL1 and NEIL2 DNA glycosylases modulate anxiety and learning in a cooperative manner in mice

Oxidative DNA damage in the brain has been implicated in neurodegeneration and cognitive decline. DNA glycosylases initiate base excision repair (BER), the main pathway for oxidative DNA base lesion repair. NEIL1 and NEIL3 DNA glycosylases affect cognition in mice, while the role of NEIL2 remains unclear. Here, we investigate the impact of NEIL2 and its potential overlap with NEIL1 on behavior in knockout mouse models. Neil1^-/-Neil2^-/- mice display hyperactivity, reduced anxiety and improved learning. Hippocampal oxidative DNA base lesion levels are comparable between genotypes and no mutator phenotype is found. Thus, impaired canonical repair is not likely to explain the altered behavior. Electrophysiology suggests reduced axonal activation in the hippocampal CA1 region in Neil1^-/-Neil2^-/- mice and lack of NEIL1 and NEIL2 causes dysregulation of genes in CA1 relevant for synaptic function. We postulate a cooperative function of NEIL1 and NEIL2 in genome regulation, beyond canonical BER, modulating behavior in mice.

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.

Integrating single-cell transcriptomics and microcircuit computer modeling

Biophysically realistic computer modeling of neuronal microcircuitry has served as a testing ground for hypotheses related to the structure and function of different brain microcircuits. Recent advances in single-cell transcriptomics provide snapshots of a neuron's molecular state and have demonstrated that cell-specific genetic markers engineer the electrophysiological properties of a neuron. Integrating these molecular details with biophysical modeling can allow unprecedented mechanistic insights. In this opinion review, we consider systems biology-based strategies involving statistical deconvolution and gene ontology to integrate the two approaches. We foresee that this integration will infer the nonlinear interactions between the transcriptomically detailed neurons in different brain states. For an initial assessment of these integrative strategies, we recommend testing them on a penetrant phenotype such as epilepsy or a basic organism model such as Caenorhabditis elegans.

Visualization and modeling of inhibition of IL-1β and TNF-α mRNA transcription at the single-cell level

IL-1β and TNF-α are canonical immune response mediators that play key regulatory roles in a wide range of inflammatory responses to both chronic and acute conditions. Here we employ an automated microscopy platform for the analysis of messenger RNA (mRNA) expression of IL-1β and TNF-α at the single-cell level. The amount of IL-1β and TNF-α mRNA expressed in a human monocytic leukemia cell line (THP-1) is visualized and counted using single-molecule fluorescent in-situ hybridization (smFISH) following exposure of the cells to lipopolysaccharide (LPS), an outer-membrane component of Gram-negative bacteria. We show that the small molecule inhibitors MG132 (a 26S proteasome inhibitor used to block NF-κB signaling) and U0126 (a MAPK Kinase inhibitor used to block CCAAT-enhancer-binding proteins C/EBP) successfully block IL-1β and TNF-α mRNA expression. Based upon this single-cell mRNA expression data, we screened 36 different mathematical models of gene expression, and found two similar models that capture the effects by which the drugs U0126 and MG132 affect the rates at which the genes transition into highly activated states. When their parameters were informed by the action of each drug independently, both models were able to predict the effects of the combined drug treatment. From our data and models, we postulate that IL-1β is activated by both NF-κB and C/EBP, while TNF-α is predominantly activated by NF-κB. Our combined single-cell experimental and modeling efforts show the interconnection between these two genes and demonstrates how the single-cell responses, including the distribution shapes, mean expression, and kinetics of gene expression, change with inhibition.

Unraveling Heterogeneity in Transcriptome and Its Regulation Through Single-Cell Multi-Omics Technologies

Cellular heterogeneity plays a pivotal role in tissue homeostasis and the disease development of multicellular organisms. To deconstruct the heterogeneity, a multitude of single-cell toolkits measuring various cellular contents, including genome, transcriptome, epigenome, and proteome, have been developed. More recently, multi-omics single-cell techniques enable the capture of molecular footprints with a higher resolution by simultaneously profiling various cellular contents within an individual cell. Integrative analysis of multi-omics datasets unravels the relationships between cellular modalities, builds sophisticated regulatory networks, and provides a holistic view of the cell state. In this review, we summarize the major developments in the single-cell field and review the current state-of-the-art single-cell multi-omic techniques and the bioinformatic tools for integrative analysis.

Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy

Single‐cell RNA sequencing (scRNA‐seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA‐seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost‐effective single‐cell isolation methods with higher throughput as well as ongoing technological development of scRNA‐seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA‐seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA‐seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA‐seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell‐cell communications, regulatory single‐cell states, immune cell distributions, and more. scRNA‐seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA‐seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA‐seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA‐seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA‐seq technologies and technical problems that remain to be overcome.

High-Throughput Identification and Screening of Single Microbial Cells by Nanobowl Array

High-throughput screening and fast identification of single bacterial cells are crucial for clinical diagnosis, bioengineering, and fermentation engineering. Although single-cell technologies have been developed extensively in recent years, the single-cell technologies for bacteria still need further exploration. In this study, we demonstrate an identification and screening technology for single bacterial cells based on a large-scale nanobowl array, which is well-ordered and size-adjustable for use with different kinds of bacteria. When the culture medium with monodispersed bacteria was placed on the nanobowl array, it successfully enabled loading of single bacterium into a single nanobowl. Because of the limitative size and depth of the nanobowls, mixture of different bacteria species could be screened according to their sizes. In addition, with the help of a low electrical current, the bacteria can be further screened according to their intrinsic surface charges. If combined with micromanipulation technology, high-throughput single bacterial selection can be achieved in future.

TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis

In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST .

Single-cell RNA sequencing for the study of development, physiology and disease

An ongoing technological revolution is continually improving our ability to carry out very high-resolution studies of gene expression patterns. Current technology enables the global gene expression profiles of single cells to be defined, facilitating dissection of heterogeneity in cell populations that was previously hidden. In contrast to gene expression studies that use bulk RNA samples and provide only a virtual average of the diverse constituent cells, single-cell studies enable the molecular distinction of all cell types within a complex population mix, such as a tumour or developing organ. For instance, single-cell gene expression profiling has contributed to improved understanding of how histologically identical, adjacent cells make different differentiation decisions during development. Beyond development, single-cell gene expression studies have enabled the characteristics of previously known cell types to be more fully defined and facilitated the identification of novel categories of cells, contributing to improvements in our understanding of both normal and disease-related physiological processes and leading to the identification of new treatment approaches. Although limitations remain to be overcome, technology for the analysis of single-cell gene expression patterns is improving rapidly and beginning to provide a detailed atlas of the gene expression patterns of all cell types in the human body.

The new technologies of high-throughput single-cell RNA sequencing

A wealth of genome and transcriptome data obtained using new generation sequencing (NGS) technologies for whole organisms could not answer many questions in oncology, immunology, physiology, neurobiology, zoology and other fields of science and medicine. Since the cell is the basis for the living of all unicellular and multicellular organisms, it is necessary to study the biological processes at its level. This understanding gave impetus to the development of a new direction – the creation of technologies that allow working with individual cells (single-cell technology). The rapid development of not only instruments, but also various advanced protocols for working with single cells is due to the relevance of these studies in many fields of science and medicine. Studying the features of various stages of ontogenesis, identifying patterns of cell differentiation and subsequent tissue development, conducting genomic and transcriptome analyses in various areas of medicine (especially in demand in immunology and oncology), identifying cell types and states, patterns of biochemical and physiological processes using single cell technologies, allows the comprehensive research to be conducted at a new level. The first RNA-sequencing technologies of individual cell transcriptomes (scRNA-seq) captured no more than one hundred cells at a time, which was insufficient due to the detection of high cell heterogeneity, existence of the minor cell types (which were not detected by morphology) and complex regulatory pathways. The unique techniques for isolating, capturing and sequencing transcripts of tens of thousands of cells at a time are evolving now. However, new technologies have certain differences both at the sample preparation stage and during the bioinformatics analysis. In the paper we consider the most effective methods of multiple parallel scRNA-seq using the example of 10XGenomics, as well as the specifics of such an experiment, further bioinformatics analysis of the data, future outlook and applications of new high-performance technologies.

Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method to breast cancer DCCs of a patient undergoing anti-HER2-directed therapy. Here, we were able to measure ERBB2 expression levels in all HER2-protein-positive DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs.

Angiogenic Gene Profiles in Laser-Microdissected Microvessels and Neurons from Ischemic Penumbra of Rat Brain

Angiogenesis is induced immediately after cerebral ischemia and plays a pivotal role in the strategy against ischemic injury. We hypothesized that the coordinated interaction between microvessels and neurons was altered immediately after stroke, and microvessels and neurons would show the temporal specificity of angiogenic gene profiles after cerebral ischemia. Microvessels and neurons were harvested in the ischemic penumbra of rat brain using the PixCell II laser capture microdissection (LCM) instrument. After RNA isolation, T7 and gene-specific primer RNA linear amplification were performed, and angiogenic functional grouping cDNA profiling was analyzed in LCM samples. cDNA microarray results showed there were 35 (36.46%) and 27 (28.13%) genes expression changes in the microvessels, while 25 (26.04%) and 31 (32.29%) genes were changed in the neurons at 2 h and 24 h after cerebral ischemia. Members of growth factors and receptors, cytokines and chemokines, adhesion molecules, matrix proteins, proteases, and inhibitors showed temporal and spatial differentiation in the microvessels and neurons after cerebral ischemia. This finding will help to understand the coordination and interaction between microvessels and neurons, and to elucidate the molecular mechanisms of angiogenesis after brain ischemic injury.

Quantitative Single-Cell Analysis of Isolated Cancer Cells with a Microwell Array

The heterogeneous nature of tumor-cell populations suggests that quantitative analysis at the single-cell level may provide better insights into cancer biology. Specifically, detection of multiple biomarkers from a single cell offers important initial information about cellular behavior. However, conventional approaches limit biomarker detection at the single-cell level. Here, we fabricated a polymer microwell array to capture single cells from prostate-cancer cell lines and quantitatively analyzed the expression of three different cancer-related biomarkers, CD44, EpCAM, and PSMA, without a membrane protein-extraction step. The resulting information on cell-surface biomarker distributions was compared with that from other standard analytical techniques. Interestingly, a large variation in CD44-expression levels was observed when the cell-proliferation cycle was modulated. On the other hand, the expression levels of EpCAM in three different cell lines are consistent among the different analytical methods with the exception of the microarray, where it has a different substrate material to adhere to. This observation clearly emphasizes that biomarker choice and environmental control are critical for properly understanding the single-cell state.

Strategies for Converting RNA to Amplifiable cDNA for Single-Cell RNA Sequencing Methods

This review describes the features of molecular biology techniques for single-cell RNA sequencing (scRNA-seq), including methods developed in our laboratory. Existing scRNA-seq methods require the conversion of first-strand cDNA to amplifiable cDNA followed by whole-transcript amplification. There are three primary strategies for this conversion: poly-A tagging, template switching, and RNase H-DNA polymerase I-mediated second-strand cDNA synthesis for in vitro transcription. We discuss the merits and limitations of these strategies and describe our Reverse Transcription with Random Displacement Amplification technology that allows for direct first-strand cDNA amplification from RNA without the need for conversion to an amplifiable cDNA. We believe that this review provides all users of single-cell transcriptome technologies with an understanding of the relationship between the quantitative performance of various methods and their molecular features.

A potential mechanism for first-person internal sensation of memory provides evidence for the relationship between learning and LTP induction

Studies conducted to verify learning-induced changes anticipated from Hebb's postulate led to the finding of long-term potentiation (LTP). Even though several correlations have been found between behavioural markers of memory retrieval and LTP, it is not known how memories are retrieved using learning-induced changes. In this context, the following non-correlated findings between learning and LTP induction provide constraints for discovering the mechanism: 1) Requirement of high stimulus intensity for LTP induction in contrast to what is expected for a learning mechanism, 2) Delay of at least 20 to 30 s from stimulation to LTP induction, in contrast to mere milliseconds for associative learning, and 3) A sudden drop in peak-potentiated effect (short-term potentiation) that matches with short-lasting changes expected during working memory and occurs only at the time of delayed LTP induction. When memories are viewed as first-person internal sensations, a newly uncovered mechanism provides explanation for the relationship between memory and LTP. This work interconnects large number of findings from the fields of neuroscience and psychology and provides a further verifiable mechanism of learning.

Of local translation control and lipid signaling in neurons

Fine-tuned regulation of new proteins synthesis is key to the fast adaptation of cells to their changing environment and their response to external cues. Protein synthesis regulation is particularly refined and important in the case of highly polarized cells like neurons where translation occurs in the subcellular dendritic compartment to produce long-lasting changes that enable the formation, strengthening and weakening of inter-neuronal connection, constituting synaptic plasticity. The changes in local synaptic proteome of neurons underlie several aspects of synaptic plasticity and new protein synthesis is necessary for long-term memory formation. Details of how neuronal translation is locally controlled only start to be unraveled. A generally accepted view is that mRNAs are transported in a repressed state and are translated locally upon externally cued triggering signaling cascades that derepress or activate translation machinery at specific sites. Some important yet poorly considered intermediates in these cascades of events are signaling lipids such as diacylglycerol and its balancing partner phosphatidic acid. A link between these signaling lipids and the most common inherited cause of intellectual disability, Fragile X syndrome, is emphasizing the important role of these secondary messages in synaptically controlled translation.

Patterning Hydrophobic Surfaces by Negative Microcontact Printing and Its Applications

Here, a negative microcontact printing method is developed to form hydrophilic polydopamine (PDA) patterns with micrometer resolution on hydrophobic including perfluorinated surfaces. In the process of the negative microcontact printing, a uniform PDA thin film is first formed on the hydrophobic surface. An activated polydimethylsiloxane (PDMS) stamp is then placed in contact with the PDA-coated hydrophobic surface. Taking advantage of the difference in the surface energy between the hydrophobic surface and the stamp, PDA is removed from the contact area after the stamp release. As a result, a PDA pattern complementary to the stamp is obtained on the hydrophobic surface. By using the negative microcontact printing, arrays of liquid droplets and single cells are reliably formed on perfluorinated surfaces. Microlens array with tunable focal length for imaging studies is further created based on the droplet array. The negative microcontact printing method is expected to be widely applicable in high-throughput chemical and biological screening and analysis.

Selective decline of neurotrophin and neurotrophin receptor genes within CA1 pyramidal neurons and hippocampus proper: Correlation with cognitive performance and neuropathology in mild cognitive impairment and Alzheimer's disease

Hippocampal CA1 pyramidal neurons, a major component of the medial temporal lobe memory circuit, are selectively vulnerable during the progression of Alzheimer's disease (AD). The cellular mechanism(s) underlying degeneration of these neurons and the relationship to cognitive performance remains largely undefined. Here, we profiled neurotrophin and neurotrophin receptor gene expression within microdissected CA1 neurons along with regional hippocampal dissections from subjects who died with a clinical diagnosis of no cognitive impairment (NCI), mild cognitive impairment (MCI), or AD using laser capture microdissection (LCM), custom-designed microarray analysis, and qPCR of CA1 subregional dissections. Gene expression levels were correlated with cognitive test scores and AD neuropathology criteria. We found a significant downregulation of several neurotrophin genes (e.g., Gdnf, Ngfb, and Ntf4) in CA1 pyramidal neurons in MCI compared to NCI and AD subjects. In addition, the neurotrophin receptor transcripts TrkB and TrkC were decreased in MCI and AD compared to NCI. Regional hippocampal dissections also revealed select neurotrophic gene dysfunction providing evidence for vulnerability within the hippocampus proper during the progression of dementia. Downregulation of several neurotrophins of the NGF family and cognate neurotrophin receptor (TrkA, TrkB, and TrkC) genes correlated with antemortem cognitive measures including the Mini-Mental State Exam (MMSE), a composite global cognitive score (GCS), and Episodic, Semantic, and Working Memory, Perceptual Speed, and Visuospatial domains. Significant correlations were found between select neurotrophic expression downregulation and neuritic plaques (NPs) and neurofibrillary tangles (NFTs), but not diffuse plaques (DPs). These data suggest that dysfunction of neurotrophin signaling complexes have profound negative sequelae within vulnerable hippocampal cell types, which play a role in mnemonic and executive dysfunction during the progression of AD.

Distinct stages of synapse elimination are induced by burst firing of CA1 neurons and differentially require MEF2A/D

Experience and activity refine cortical circuits through synapse elimination, but little is known about the activity patterns and downstream molecular mechanisms that mediate this process. We used optogenetics to drive individual mouse CA1 hippocampal neurons to fire in theta frequency bursts to understand how cell autonomous, postsynaptic activity leads to synapse elimination. Brief (1 hr) periods of postsynaptic bursting selectively depressed AMPA receptor (R) synaptic transmission, or silenced excitatory synapses, whereas more prolonged (24 hr) firing depressed both AMPAR and NMDAR EPSCs and eliminated spines, indicative of a synapse elimination. Both synapse silencing and elimination required de novo transcription, but only silencing required the activity-dependent transcription factors MEF2A/D. Burst firing induced MEF2A/D-dependent induction of the target gene Arc which contributed to synapse silencing and elimination. This work reveals new and distinct forms of activity and transcription-dependent synapse depression and suggests that these processes can occur independently.

Local pH Monitoring of Small Cluster of Cells using a Fiber-Optic Dual-Core Micro-Probe

Biological studies of tissues and cells have enabled numerous discoveries, but these studies still bear potential risks of invalidation because of cell heterogeneity. Through high-accuracy techniques, recent studies have demonstrated that discrepancies do exist between the results from low-number-cell studies and cell-population-based results. Thus the urgent need to re-evaluate key principles on limited number of cells has been provoked. In this study, a novel designed dual-core fiber-optic pH micro-probe was fabricated and demonstrated for niche environment pH sensing with high spatial resolution. An organic-modified silicate (OrMoSils) sol-gel thin layer was functionalized by entrapping a pH indicator, 2', 7'-Bis (2-carbonylethyl)-5(6)-carboxyfluorescein (BCECF), on a ~70 μm sized probe tip. Good linear correlation between fluorescence ratio of I560 nm/I640 nm and intercellular pH values was obtained within a biological-relevant pH range from 6.20 to 7.92 (R2 = 0.9834), and with a pH resolution of 0.035 ± 0.005 pH units. The probe's horizontal spatial resolution was demonstrated to be less than 2mm. Moreover, the probe was evaluated by measuring the localized extracellular pH changes of cultured human lung cancer cells (A549) when exposed to titanium dioxide nanoparticles (TiO2 NPs). Results showed that the probe has superior capability for fast, local, and continual monitoring of a small cluster of cells, which provides researchers a fast and accurate technique to conduct local pH measurements for cell heterogeneity-related studies.

Studying hematopoiesis using single-cell technologies

Hematopoiesis is probably the best-understood stem cell differentiation system; hematopoietic stem cell (HSC) transplantation represents the most widely used regenerative therapy. The classical view of lineage hierarchy in hematopoiesis is built on cell type definition system by a group of cell surface markers. However, the traditional model is facing increasing challenges, as many classical cell types are proved to be heterogeneous. Recently, the developments of new technologies allow genome, transcriptome, proteome, and epigenome analysis at the single-cell level. For the first time, we can study hematopoietic system at single-cell resolution on a multi-omic scale. Here, we review recent technical advances in single-cell analysis technology, as well as their current applications. We will also discuss the impact of single-cell technologies on both basic research and clinical application in hematology.

Neurodegenerative disorders share common features of "loss of function" states of a proposed mechanism of nervous system functions

Neurodegenerative disorders are highly heterogeneous for the locations affected and the nature of the aggregated proteins. Nearly 80% of the neurodegenerative disorders occur sporadically, indicating that certain factors must combine to initiate the degenerative changes. The contiguous extension of degenerative changes from cell to cell, the association with viral fusion proteins, loss of dendritic spines (postsynaptic terminals), and the eventual degeneration of cells indicate the presence of a unique mechanism for inter-cellular spread of pathology. It is not known whether the "loss of function" states of the still unknown normal nervous system operations can lead to neurodegenerative disorders. Here, the possible loss of function states of a proposed normal nervous system function are examined. A reversible inter-postsynaptic functional LINK (IPL) mechanism, consisting of transient inter-postsynaptic membrane (IPM) hydration exclusion and partial to complete IPM hemifusions, was proposed as a critical step necessary for the binding process and the induction of internal sensations of higher brain functions. When various findings from different neurodegenerative disorders are systematically organized and examined, disease features match the effects of loss of function states of different IPLs. Changes in membrane composition, enlargement of dendritic spines by dopamine and viral fusion proteins are capable of altering the IPLs to form IPM fusion. The latter can lead to the observed lateral spread of pathology, inter-neuronal cytoplasmic content mixing and abnormal protein aggregation. Since both the normal mechanism of reversible IPM hydration exclusion and the pathological process of transient IPM fusion can evade detection, testing their occurrence may provide preventive and therapeutic opportunities for these disorders.

Cellular Deconstruction: Finding Meaning in Individual Cell Variation

The advent of single cell transcriptome analysis has permitted the discovery of cell-to-cell variation in transcriptome expression of even presumptively identical cells. We hypothesize that this variability reflects a many-to-one relation between transcriptome states and the phenotype of a cell. In this relation, the molecular ratios of the subsets of RNA are determined by the stoichiometric constraints of the cell systems, which underdetermine the transcriptome state. Furthermore, the variability is, in part, induced by the tissue context and is important for system-level function. This theory is analogous to theories of literary deconstruction, where multiple 'signifiers' work in opposition to one another to create meaning. By analogy, transcriptome phenotypes should be defined as subsets of RNAs comprising selected RNA systems where the system-associated RNAs are balanced with each other to produce the associated cellular function. This idea provides a framework for understanding cellular heterogeneity in phenotypic responses to variant conditions, such as disease challenge.

Rapid chain generation of interpostsynaptic functional LINKs can trigger seizure generation: Evidence for potential interconnections from pathology to behavior

The experimental finding that a paroxysmal depolarizing shift (PDS), an electrophysiological correlate of seizure activity, is a giant excitatory postsynaptic potential (EPSP) necessitates a mechanism for spatially summating several EPSPs at the level of the postsynaptic terminals (dendritic spines). In this context, we will examine reversible interpostsynaptic functional LINKs (IPLs), a proposed mechanism for inducing first-person virtual internal sensations of higher brain functions concurrent with triggering behavioral motor activity for possible pathological changes that may contribute to seizures. Pathological conditions can trigger a rapid chain generation and propagation of different forms of IPLs leading to seizure generation. A large number of observations made at different levels during both ictal and interictal periods are explained by this mechanism, including the tonic and clonic motor activity, different types of hallucinations, loss of consciousness, gradual worsening of cognitive abilities, a relationship with kindling (which uses an augmented stimulation protocol than that used for inducing long-term potentiation (LTP), which is an electrophysiological correlate of behavioral makers of internal sensation of memory), effect of a ketogenic diet on seizure prevention, dendritic spine loss in seizure disorders, neurodegenerative changes, and associated behavioral changes. The interconnectable nature of these findings is explained as loss of function states of a proposed normal functioning of the nervous system.

Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons

Clarifying gene expression in narrowly defined neuronal populations can provide insight into cellular identity, computation, and functionality. Here, we used next-generation RNA sequencing (RNA-seq) to produce a quantitative, whole genome characterization of gene expression for the major excitatory neuronal classes of the hippocampus; namely, granule cells and mossy cells of the dentate gyrus, and pyramidal cells of areas CA3, CA2, and CA1. Moreover, for the canonical cell classes of the trisynaptic loop, we profiled transcriptomes at both dorsal and ventral poles, producing a cell-class- and region-specific transcriptional description for these populations. This dataset clarifies the transcriptional properties and identities of lesser-known cell classes, and moreover reveals unexpected variation in the trisynaptic loop across the dorsal-ventral axis. We have created a public resource, Hipposeq (http://hipposeq.janelia.org), which provides analysis and visualization of these data and will act as a roadmap relating molecules to cells, circuits, and computation in the hippocampus.

DOI:http://dx.doi.org/10.7554/eLife.14997.001

Both mouse and human brains are made up of many millions of cells called neurons that are interconnected to form circuits. These neurons are not all the same, because different classes of neurons express different complements of genes. Neurons that express similar genes tend to look and act alike, whereas neurons that express different genes tend to be dissimilar.

Cembrowski et al. have used a technique called next-generation RNA sequencing (RNA-seq) to determine which genes are expressed in groups of neurons that represent the main cell types found in a part of the brain called the hippocampus. This brain region is important for memory, and was chosen because the location and appearance of the main cell types in the hippocampus were already well understood.

The approach revealed that the main types of neurons in the mouse hippocampus are all very different from each other in terms of gene expression, and that even neurons of the same type can exhibit large differences across the hippocampus. Cembrowski et al. created a website that will allow other researchers to easily navigate, analyze, and visualize gene expression data in these populations of neurons.

Future work could next make use of recent technological advances to analyze gene expression in individual neurons, rather than groups of cells, to provide an even more detailed picture. It is also hoped that understanding the differences in gene expression will guide examination of how the hippocampus contributes to memory and what goes wrong in diseases that affect this region of the brain.

DOI:http://dx.doi.org/10.7554/eLife.14997.002

A retrograde adeno-associated virus for collecting ribosome-bound mRNA from anatomically defined projection neurons

The brain contains a large variety of projection neurons with different functional properties. The functional properties of projection neurons arise from their connectivity with other neurons and their molecular composition. We describe a novel tool for obtaining the gene expression profiles of projection neurons that are anatomically defined by the location of their soma and axon terminals. Our tool utilizes adeno-associated virus serotype 9 (AAV9), which we found to retrogradely transduce projection neurons after injection at the site of the axon terminals. We used AAV9 to express Enhanced Green Fluorescent Protein (EGFP)-tagged ribosomal protein L10a (EGFP-L10a), which enables the immunoprecipitation of EGFP-tagged ribosomes and associated mRNA with a method known as Translating Ribosome Affinity Purification (TRAP). To achieve high expression of the EGFP-L10a protein in projection neurons, we placed its expression under control of a 1.3 kb alpha-calcium/calmodulin-dependent protein kinase II (Camk2a) promoter. We injected the AAV9-Camk2a-TRAP virus in either the hippocampus or the bed nucleus of the stria terminalis (BNST) of the mouse brain. In both brain regions the 1.3 kb Camk2a promoter did not confer complete cell-type specificity around the site of injection, as EGFP-L10a expression was observed in Camk2a-expressing neurons as well as in neuronal and non-neuronal cells that did not express Camk2a. In contrast, cell-type specific expression was observed in Camk2a-positive projection neurons that were retrogradely transduced by AAV9-Camk2a-TRAP. Injection of AAV9-Camk2a-TRAP into the BNST enabled the use of TRAP to collect ribosome-bound mRNA from basal amygdala projection neurons that innervate the BNST. AAV9-Camk2a-TRAP provides a single-virus system that can be used for the molecular profiling of anatomically defined projection neurons in mice and other mammalian model organisms. In addition, AAV9-Camk2a-TRAP may enable the discovery of protein synthesis events that support information storage in projection neurons.

A pressure-reversible cellular mechanism of general anesthetics capable of altering a possible mechanism for consciousness

Different anesthetics are known to modulate different types of membrane-bound receptors. Their common mechanism of action is expected to alter the mechanism for consciousness. Consciousness is hypothesized as the integral of all the units of internal sensations induced by reactivation of inter-postsynaptic membrane functional LINKs during mechanisms that lead to oscillating potentials. The thermodynamics of the spontaneous lateral curvature of lipid membranes induced by lipophilic anesthetics can lead to the formation of non-specific inter-postsynaptic membrane functional LINKs by different mechanisms. These include direct membrane contact by excluding the inter-membrane hydrophilic region and readily reversible partial membrane hemifusion. The constant reorganization of the lipid membranes at the lateral edges of the postsynaptic terminals (dendritic spines) resulting from AMPA receptor-subunit vesicle exocytosis and endocytosis can favor the effect of anesthetic molecules on lipid membranes at this location. Induction of a large number of non-specific LINKs can alter the conformation of the integral of the units of internal sensations that maintain consciousness. Anesthetic requirement is reduced in the presence of dopamine that causes enlargement of dendritic spines. Externally applied pressure can transduce from the middle ear through the perilymph, cerebrospinal fluid, and the recently discovered glymphatic pathway to the extracellular matrix space, and finally to the paravenular space. The pressure gradient reduce solubility and displace anesthetic molecules from the membranes into the paravenular space, explaining the pressure reversal of anesthesia. Changes in membrane composition and the conversion of membrane hemifusion to fusion due to defects in the checkpoint mechanisms can lead to cytoplasmic content mixing between neurons and cause neurodegenerative changes. The common mechanism of anesthetics presented here can operate along with the known specific actions of different anesthetics.

The technology and biology of single-cell RNA sequencing

The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.

High spatial resolution proteomic comparison of the brain in humans and chimpanzees

We performed high-throughput mass spectrometry at high spatial resolution from individual regions (anterior cingulate and primary motor, somatosensory, and visual cortices) and layers of the neocortex (layers III, IV, and V) and cerebellum (granule cell layer), as well as the caudate nucleus in humans and chimpanzees. A total of 39 mass spectrometry peaks were matched with probable protein identifications in both species, allowing for comparison in expression. We explored how the pattern of protein expression varies across regions and cortical layers to provide insights into the differences in molecular phenotype of these neural structures between species. The expression of proteins differed principally in a region- and layer-specific pattern, with more subtle differences between species. Specifically, human and chimpanzee brains were similar in their distribution of proteins related to the regulation of transcription and enzyme activity but differed in their expression of proteins supporting aerobic metabolism. Whereas most work assessing molecular expression differences in the brains of primates has been performed on gene transcripts, this dataset extends current understanding of the differential molecular expression that may underlie human cognitive specializations.

Microdissection and transcriptional profiling: a window into the pathobiology of preclinical prion disease

Prion diseases share common features on a sub-cellular level with many neurodegenerative diseases including Alzheimer disease; the most prevalent neurodegenerative disease world-wide. The most obvious similarity is the accumulation of misfolded forms of the host proteins which forms aggregates in the brains of patients. Remarkably, one of the earliest pathological changes detected in degenerating brain tissue, well before clinical symptoms are observed, is synaptic dysfunction and loss. This pathology was recently shown to be reversible in early stages of mouse prion disease suggesting that synaptic regeneration and reestablishment of neuronal function is possible. Determination of the molecular events that underlie synapse degeneration and how this eventually results in neuronal loss is therefore a research priority that may contribute to the search for new therapeutic interventions for neurodegenerative disorders. Functional genomic studies using unbiased whole genome expression analyses represent one method that can provide insights into these perplexing processes. However, transcriptional profiles from brain tissues are representative of a heterogeneous mixture of cell types that effectively mask the expression of low abundance transcripts, or molecular changes that occur only in a small population of affected neurons. One method that was recently applied to address these challenges was laser capture microdissection which was used to effectively isolate the CA1 neuronal rich region of the hippocampus prior to RNA extraction. Profiling of both mRNAs and microRNAs revealed previously unidentified neuronal-specific genes and expression signatures that are relevant to understanding the pathophysiological processes involved in preclinical stages of prion disease. In this review we will highlight these molecular signatures and discuss their implications with respect to prion-induced neurodegeneration.

Antisense RNA amplification for target assessment of total mRNA from a single cell

This protocol describes how to amplify mRNA isolated from a single cell and then analyze its gene expression profile using polymerase chain reaction (PCR). Single-cell analysis is advantageous over studies of cell populations because it allows identification of a range of normal physiological states expressed by different cells of the same cell type without the confounding effects of averaging that result from measuring physiological states of cell populations. This is especially important when addressing questions of physiology in tissues, which comprises many different cell types. However, a single cell does not contain enough mRNA for all of the expressed transcripts to be detected or measured by any current molecular biology techniques. The antisense RNA (aRNA) amplification method was developed to amplify the picogram amounts of mRNA found within a single cell to microgram amounts of aRNA after three rounds of amplification. This aRNA can then easily be analyzed by microarray or next-generation sequencing. These methods allow identification of all expressed mRNA species within a single cell, including previously unknown mRNAs or those mRNAs specifically affected by a certain treatment. mRNA species of interest identified by these techniques can be further analyzed by designing primers targeting these species and performing PCR. cDNA synthesized from RNA at any stage in the aRNA amplification procedure, including material directly from collected unamplified cells, can be analyzed using PCR. Regardless of downstream applications, single-cell aRNA amplification is a powerful tool for studying single-cell physiological dynamics.

Dissecting inhibitory brain circuits with genetically-targeted technologies

The evolution of genetically targeted tools has begun to allow us to dissect anatomically and functionally heterogeneous interneurons, and to probe circuit function from synapses to behavior. Over the last decade, these tools have been used widely to visualize neurons in a cell type-specific manner, and engage them to activate and inactivate with exquisite precision. In this process, we have expanded our understanding of interneuron diversity, their functional connectivity, and how selective inhibitory circuits contribute to behavior. Here we discuss the relative assets of genetically encoded fluorescent proteins (FPs), viral tracing methods, optogenetics, chemical genetics, and biosensors in the study of inhibitory interneurons and their respective circuits.

Juxtacellular recording and morphological identification of single neurons in freely moving rats

It is well established that neural circuits consist of a great diversity of cell types, but very little is known about how neuronal diversity contributes to cognition and behavior. One approach to addressing this problem is to directly link cellular diversity to neuronal activity recorded in vivo in behaving animals. Here we describe the technical procedures for obtaining juxtacellular recordings from single neurons in trained rats engaged in exploratory behavior. The recorded neurons can be labeled to allow subsequent anatomical identification. In its current format, the protocol can be used for resolving the cellular identity of spatially modulated neurons (i.e., head-direction cells and grid cells), which form the basis of the animal's internal representation of space, but this approach can easily be extended to other unrestrained behaviors. The procedures described here, from the beginning of animal training to the histological processing of brain sections, can be completed in ≈ 3-4 weeks.

RNA amplification for pseudogene detection using RNA-seq

Transcriptomic research using microarrays and RNA-Sequencing (RNA-seq) is now possible starting from minute biological samples, such as clinical specimens or embryos, due to the development of highly sensitive and reproducible cDNA synthesis methods. Here, we describe a quick method of RNA amplification and double-stranded cDNA synthesis starting with 10 ng of high-quality total RNA extracted from porcine embryos. The final product (double-stranded DNA) is adequate for the detection by RNA-seq of protein-coding transcripts, as well as of all the other classes of noncoding RNAs, including pseudogenes.

Proteostasis in complex dendrites

Like all cells, neurons are made of proteins that have characteristic synthesis and degradation profiles. Unlike other cells, however, neurons have a unique multipolar architecture that makes ∼10,000 synaptic contacts with other neurons. Both the stability and modifiability of the neuronal proteome are crucial for its information-processing, storage and plastic properties. The cell biological mechanisms that synthesize, modify, deliver and degrade dendritic and synaptic proteins are not well understood but appear to reflect unique solutions adapted to the particular morphology of neurons.

Genomics and proteomics in solving brain complexity

The human brain is extraordinarily complex, composed of billions of neurons and trillions of synaptic connections. Neurons are organized into circuit assemblies that are modulated by specific interneurons and non-neuronal cells, such as glia and astrocytes. Data on human genome sequences predicts that each of these cells in the human brain has the potential of expressing ∼20 000 protein coding genes and tens of thousands of noncoding RNAs. A major challenge in neuroscience is to determine (1) how individual neurons and circuitry utilize this potential during development and maturation of the nervous system, and for higher brain functions such as cognition, and (2) how this potential is altered in neurological and psychiatric disorders. In this review, we will discuss how recent advances in next generation sequencing, proteomics and bioinformatics have transformed our understanding of gene expression and the functions of neural circuitry, memory storage, and disorders of cognition.